卡內基梅隆大學機器人研究所課程分享
卡內基梅隆大學機器人研究所課程分享（www.ri.cmu.edu/education/courses/）
機器翻譯如下：
課程大綱
目前已經開設和即將開設的機器人課程的完整和準確的時間表，更多大學相關課程，可以通過訪問大學課程頁面訪問進行查詢。本頁面概述了機器人部門教授的課程。
所有帶有“16-”字首的課程均由機器人部門提供。其他提供機器人學教授課程的部門是電腦科學（CS），電氣和計算機工程（ECE），機械工程（MechE），統計學（Stat），心理學（心理學），泰珀商學院（GSIA）和複雜工程系統研究所（ICES）。
在網站上查詢已經註冊的機器人16-xxx課程的基本流程如下：
- 轉到：課程安排課程搜尋。
- 選擇“學期/年”
- 從部門列表中選擇“SCS：Robotics（16-xxx）”。
- 點選“RETRIEVE SCHEDULE”
研究生課程
16-623 高階計算機視覺應用程式
教授： Simon Lucey
課程描述：
計算機視覺是一種試圖從影象和視訊中提取資訊的學科。地球上幾乎每個智慧裝置都有一個攝像頭，人們越來越關注如何開發使用計算機視覺的應用程式來執行不斷擴充套件的事物列表，包括：3D對映，照片/影象搜尋，人/物體跟蹤，擴增實境等。本課程面向熟悉計算機視覺的研究生，並希望瞭解更多有關智慧裝置和嵌入式系統中應用最先進的視覺方法的知識。強大的程式設計背景是必須的（至少對C / C ++有很好的瞭解），主題將包括使用傳統的計算機視覺軟體工具（OpenCV，MATLAB工具箱，VLFeat，CAFFE，Torch 7），使用移動視覺庫（如GPUImage，Metal和快速數學庫，如Armadillo和Eigen）在iOS裝置上進行開發。為了保持一致性，所有應用程式開發都將在iOS中進行，並且預計參與該類的所有學生都可以訪問執行OS X Mavericks或更高版本的基於Intel的MAC。雖然課程將集中在一個作業系統上，但從這個課程中獲得的知識很容易推廣到其他移動平臺，如Android等。
16-642 操作、估計和控制
教授： George A. Kantor
課程描述：
本課程概述了當前允許機器人進行定位並與世界互動的技術。機電系統的運動學和動力學將特別關注它們在機器人手臂上的應用。將討論機器人控制的一些基本原理，從獨立聯合PID跟蹤到耦合計算扭矩方法。機器人移動的實踐和理論將通過各種移動機器人平臺進行調查，包括輪式和履帶式車輛以及腿式機器人。通過實際演示和實驗作業，將提供課堂上一些主題的實踐經驗。請注意，本課程僅適用於MRSD學生。
預備要求：
- 僅限MRSD學生
16-650 機器人系統工程與管理
課程描述：
實際上我們周圍的一切都是一個系統 - 從口袋裡的手機到天空中的國際太空系統。系統的複雜性越高，其建立者就越多地將正式流程應用於其在“系統工程”下統稱的開發流程中受益。系統工程是一個正式的學科，指導產品從概念和設計到所有生產，營銷，服務和處置的方式。在本課程中，我們將研究系統工程的基本要素，因為它們適用於機器人系統的開發。我們將涵蓋需求分析，需求獲取和形式化，系統架構開發，貿易研究，驗證和驗證等主題。此外，對於本課程，我們將涵蓋專案管理的核心主題，必須與系統工程一起執行，以實現成功的專案和產品。對於專案管理，我們將涵蓋工作分解結構，計劃，估算和風險管理。我們將在專案管理中研究經典和敏捷方法。學生將在MRSD專案課程I和II中應用本課程的大部分內容，從而使他們有機會將理論付諸實踐，進行真實的產品設計活動。請注意，本課程僅適用於MRSD學生。（過去的專案示例：http：//mrsd.ri.cmu.edu/project-examples/）對於專案管理，我們將涵蓋工作分解結構，計劃，估算和風險管理。我們將在專案管理中研究經典和敏捷方法。學生將在MRSD專案課程I和II中應用本課程的大部分內容，從而使他們有機會將理論付諸實踐，進行真實的產品設計活動。請注意，本課程僅適用於MRSD學生。（過去的專案示例：http：//mrsd.ri.cmu.edu/project-examples/）對於專案管理，我們將涵蓋工作分解結構，計劃，估算和風險管理。我們將在專案管理中研究經典和敏捷方法。學生將在MRSD專案課程I和II中應用本課程的大部分內容，從而使他們有機會將理論付諸實踐，進行真實的產品設計活動。請注意，本課程僅適用於MRSD學生。（過去的專案示例：http：//mrsd.ri.cmu.edu/project-examples/）請注意，本課程僅適用於MRSD學生。（過去的專案示例：http：//mrsd.ri.cmu.edu/project-examples/）請注意，本課程僅適用於MRSD學生。（過去的專案示例：http：//mrsd.ri.cmu.edu/project-examples/）
16-662 機器人自治
教授： Oliver Kroemer
課程描述：
機器人自主性深入研究了開發完全自治系統所需的感知，操縱，導航，規劃和學習之間的相互作用。我們將專注於家庭，零售和醫療保健等應用領域，並確定共同的主題和關鍵瓶頸。我們將討論最先進的演算法，它們的計算和硬體要求以及它們的侷限性。為了使您能夠建立端到端系統，您將學習如何解決操作任務中的混亂和不確定性，在現實場景中開發強大的物件識別演算法，在高維空間中規劃機器人軌跡，構建高效能行為引擎級別任務，並學習應用和連線這些任務以建立自動機器人系統。
預備要求：
- 16-450
16-711 運動學，動態系統和控制
教授： Hartmut Geyer和Chris Atkeson
課程描述：
運動學，動態系統和控制是機器人技術的研究生水平入門。該課程涵蓋了分析，建模和控制在物理世界中移動並操縱它的機器人機制的基本概念和方法。主要內容包括應用於剛體鏈的運動學，動力學和控制的運動學，動力學和控制的基礎知識。其他主題包括狀態估計和動態引數識別。
16-720 計算機視覺
教授： Kris M. Kitani，Srinivasa G. Narasimhan，Simon Lucey，Deva Kannan Ramanan，Yaser Ajmal Sheikh，Abhinav Gupta和Martial Hebert
課程描述：
本課程介紹計算機視覺中使用的基本技術，即分析視覺影象中的模式，以重建和理解生成它們的物件和場景。涵蓋的主題包括影象形成和表示，相機幾何和校準，計算成像，多檢視幾何，立體，影象的3D重建，運動分析，基於物理的視覺，影象分割和物件識別。該材料基於研究生水平的文字，並酌情增加研究論文。評估基於家庭作業和最終專案。家庭作業涉及大量的Matlab程式設計練習。
圖書：
- Richard Szeliski，“計算機視覺演算法與應用”，電腦科學文字，Springer ISBN：978-1-84882-934-3，書籍要求：推薦
- David Forsyth和Jean Ponce，“計算機視覺：現代方法”，Prentice Hall ISBN：0-13-085198-1，書籍要求：推薦
16-722 感測器和感測器
教授： Cameron Riviere和Mel Siegel
課程描述：
定量感知（感測）的原理和實踐由實現它們的裝置和演算法（感測器）說明。學會批判性地檢查機器人應用的感測要求，指定所需的感測器特性，分析這些規範是否可以原則上實現，比較原則上可以實現的內容與實際可以購買或構建的內容，以瞭解解決這些差異的工程因素，以及設計能夠接近實現可用感測器實際能力的感測，數字化和計算系統。評分將基於家庭作業，課堂參與和期末考試。三到四個家庭作業將是一個動手實踐的“帶回家實驗室”，用Arduino套件完成，學生將購買該套件代替購買教科書。頂級課程模組將涵蓋（1）感測器，訊號和測量科學，（2）噪聲的起源，性質和改善，（3）端到端感測系統，（4）攝像機和其他成像感測器和系統，（5）距離感應和成像，（6）導航感測器和系統，（7）班級感興趣的其他主題（如時間允許）。
16-725 醫學影象分析
教授： John Galeotti
課程描述：
學生將獲得2D，3D和4D生物醫學影象分析的理論和實踐技能，包括與一般影象分析相關的技能。將探索計算醫學影象分析的基礎，導致目前的研究將幾何和統計應用於分割，配準，視覺化和影象理解。附加和相關的主題包括去噪/恢復，形態，水平集和形狀/特徵分析。學生將使用最新版本的國家醫學圖書館洞察工具包（ITK）和SimpleITK開發實踐經驗，SimpleITK是由卡內基梅隆大學和匹茲堡大學等機構聯盟開發的流行開源軟體庫。除了影象分析，該課程將包括與放射科醫師和病理學家的互動。***講座在CMU，學生將訪問UPMC的臨床醫生。部分或全部課堂講座也可以錄影用於公開發布，但學生可以要求將其排除在分發視訊之外。16-725是一個研究生班，16-425是一個交叉上市的本科部分。16-425大大減少了對最終專案和大型家庭作業的要求，也不需要影響臨床醫生。和16-425是一個交叉上市的本科部分。16-425大大減少了對最終專案和大型家庭作業的要求，也不需要影響臨床醫生。和16-425是一個交叉上市的本科部分。16-425大大減少了對最終專案和大型家庭作業的要求，也不需要影響臨床醫生。
預備要求：
- 瞭解向量微積分，基本概率以及C ++或python，包括基本的命令列熟悉以及如何將引數傳遞給您自己的命令列程式。不需要廣泛的C ++和模板專業知識，但有些學生可能會覺得它很有幫助。
16-741 操縱力學
教授： Matthew T. Mason
課程描述：
機器人操縱器與任務互動的運動學，靜力學和動力學，側重於運動約束，重力和摩擦力的智慧使用。基於力學的自動規劃。從製造和其他領域提取的應用示例。
16-745 動態優化
教授： 克里斯阿特克森
課程描述：
本課程將介紹優化（尤其是最佳控制）在設計行為中的應用。我們將探索表示策略的方法，包括手工設計的引數函式，基函式，表格和軌跡庫。我們還將探索用於建立策略的演算法，包括引數優化和軌跡優化（一階和二階梯度方法，順序二次規劃，隨機搜尋方法，進化演算法等）。我們將討論如何處理用於建立策略的模型與實際受控系統之間的差異（評估和穩健性問題）。該課程將結合講座，學生提供的材料和專案。本課程的目標是幫助參與者找到解決問題的最有效方法。
16-761 移動機器人簡介
教授： Alonzo Kelly
課程描述：
本課程從理論和實踐的角度涵蓋移動機器人系統設計和程式設計的所有方面。介紹了控制，定位，對映，感知和規劃的基本子系統。對於每一個，討論將包括應用數學的相關方法。系統和環境行為模型構建所必需的物理方面，以及在各種情況下都被證明有價值的核心演算法。
16-778 機電設計
教授： John M. Dolan
課程描述：
機電一體化是機械，電子和計算機控制的協同整合，以實現功能系統。本課程是一個為期一學期的多學科頂點硬體專案設計經驗，其中小型（通常是四人）電氣和計算機工程，機械工程和機器人學生團隊提供最終整合系統的最終演示，能夠執行機電一體化任務。在整個學期中，學生們在實驗室裝置和子系統中進行設計，配置，實施，測試和評估，最終形成最終的整合機電一體化系統。講座將通過比較調查，操作原理以及與機制，微控制器等相關的整合設計問題來補充實驗室經驗。電子，感測器和控制元件。交叉上市課程：18-578,24-778
16-782 機器人規劃與決策
教授： Maxim Likhachev
課程描述：
規劃和決策是機器人系統自治的關鍵組成部分。這些元件負責制定從路徑規劃和運動規劃到覆蓋和任務規劃的各種決策，以及採取有助於機器人更好地瞭解周圍世界的行動。本課程研究用於機器人規劃和決策的基礎演算法技術，並研究地面和空中機器人，人形機器人，移動操縱平臺和多機器人系統的案例研究。學生將學習演算法並在一系列基於程式設計的專案中實施。
16-785 機器人技術的綜合智慧：語言，願景和規劃
教授： Jean Hyaejin哦
課程描述：
本課程涵蓋了為機器人系統構建認知智慧的主題。認知能力構成了具有推理或解決問題能力的高階人類智慧。諸如語義感知，語言理解和任務規劃之類的功能可以構建在低階別機器人自治之上，從而實現對物理平臺的自主控制。這些主題通常跨越多個技術領域，例如，視覺語言交叉和語言 - 行動/計劃基礎。本課程由50個講座和50個研討班組成。
預備要求：
- 這個類沒有明確的先決條件，但是假定人工智慧和機器學習的一般背景知識。
16-791 應用資料科學
課程描述：
本課程在實用應用的背景下探索快速發展的資料科學領域。Applied Data Science致力於實現三個主要目標。首先是優化人力資源管理者決策的效率。第二是最大限度地利用現有資料，這樣就不會錯過任何重要線索。第三是提高對資料及其產生的基礎過程的理解。本課程旨在培養在實踐中系統地實現這些目標所需的技能。學生將獲得並鞏固對最流行的當代資料科學方法的認識，並培養在應用場景中評估所研究主題的實際效用所需的直覺。
16-811 機器人數學基礎
教授： Michael Erdmann
課程描述：
本課程涵蓋了機器人學中應用數學的選定主題，取自以下列表：1。線性方程組的解。2.多項式插值和逼近。3.非線性方程的解。4.多項式的根，結論。5.通過正交函式的近似（包括傅立葉級數）。6.常微分方程的積分。7.優化。8.變化微積分（適用於力學）。9.概率和隨機過程（馬爾可夫鏈）。10.計算幾何。11.微分幾何。
16-822 計算機視覺中基於幾何的方法
教授： Martial Hebert
課程描述：
該課程側重於計算機視覺的幾何方面：影象形成的幾何形狀及其用於3D重建和校準的用途。本課程的目的是介紹開發多檢視重建演算法所需的正式工具和結果。基本工具引入了研究仿射和投影幾何，這對於影象形成模型的發展至關重要。在課程開始時還引入了其他代數工具，例如外部代數。然後，這些工具用於開發單個檢視（相機模型），兩個檢視（基本矩陣）和三個檢視（三焦張量）的幾何影象形成的正式模型; 多幅影象的三維重建; 和自動校準。
預備要求：
- 計算機視覺（16-721或同等學歷）
圖書：
- 多個影象的幾何。Faugeras和Long，麻省理工學院出版社。，書籍要求：未指定
- 計算機視覺中的多檢視幾何，Richard Hartley和Andrew Zisserman，劍橋大學出版社，2000年6月。，書籍要求：未指定
涵蓋的主題：
- 投射，仿射和歐幾里德幾何的基本原理
- 不變性和二元性
- 代數工具
- 單檢視幾何：針孔模型
- 校準技術
- 2檢視幾何：基本矩陣
- 2檢視重建
- 三檢視幾何：三焦張量
- 引數估計和不確定性
- n檢視重建
- 自校準
16-823 基於物理學的視覺方法
課程描述：
我們每天都會觀察到一系列非常明亮的光線和色彩現象，包括大氣的炫目效果，表面和材料的複雜外觀以及水下場景。長期以來，藝術家，科學家和攝影師都對這些影響著迷，並將注意力集中在捕捉和理解這些現象上。在本課程中，我們採用計算方法對這些現象進行建模和分析，我們統稱這些現象為“視覺外觀”。課程的前半部分側重於視覺外觀的物理基礎，而課程的後半部分則側重於計算機視覺，圖形和遙感等各種領域的演算法和應用，以及水下和航空成像等技術。本課程統一了物理科學中常見的概念及其在成像科學中的應用，並將包括該領域的最新研究進展。該課程還將包括攝影比賽。
預備要求：
- 計算機視覺或計算機圖形學的本科或研究生課程
16-824 視覺中基於學習的方法
教授： Abhinav Gupta和Martial Hebert
課程描述：
計算機視覺研究生研討會課程，重點是大量資料（影象，視訊和相關標籤，文字，gps位置等）的表示和推理，以實現影象理解的最終目標。我們將閱讀關於主題的經典和近期論文的折衷混合，包括：感知理論，中級視覺（分組，分割，Poselets），物件和場景識別，3D場景理解，動作識別，上下文推理，影象分析，聯合語言和視覺模型等。我們將針對上述每個主題涵蓋範圍廣泛的監督，半監督和無監督方法。
預備要求：
- 研究生計算機視覺或機器學習課程
16-831 機器人統計技術
教授： David Held，Kris M. Kitani，Michael Kaess和J. Andrew（Drew）Bagnell
課程描述：
資料驅動的學習技術現在是構建設計用於在現實世界中執行的機器人系統的重要部分。這些系統必須學會適應環境的變化，從經驗中學習，並從示範中學習。我們將特別介紹機器學習應用於機器人系統的三個重要子領域：（1）我們將介紹線上學習，它可以用來使機器人系統適應不斷變化的環境條件。（2）我們將介紹強化學習，其中考慮了探索和開發之間的權衡，以學習如何與環境互動。我們還將介紹真實世界機器人系統中的深度強化學習技術。（3）我們將涵蓋學徒學習（模仿學習和反向強化學習），這對於教授機器人系統從專家行為中學習至關重要。先修課程：線性代數，多元微積分，概率論。
16-833 機器人定位和對映
教授： Michael Kaess
課程描述：
機器人定位和對映是在現實世界中執行的移動機器人的基本功能。比這些個別問題更具挑戰性的是它們的組合：同步定位和對映（SLAM）。需要穩健且可擴充套件的解決方案，以處理感測器測量中固有的不確定性，同時實時提供定位和地圖估計。我們將線上性代數和概率圖形模型的交叉點探索合適的有效概率推理演算法。我們還將探索最先進的系統。
涵蓋的主題：
- 同步定位和對映（SLAM）
16-843 操縱演算法
課程描述：
這是一個關於理論和演算法的高階研究生課程，使機器人可以自己或與人合作，實際操縱他們的世界。該課程將首先關注操作的功能方面，例如為靈巧的手和這些空間中的運動規劃合成穩健和穩定的掌握，以及學習操作，例如如何從演示和經驗預測穩定的掌握。展望未來，我們將討論與人們協同執行操作任務所產生的其他要求：從運動的純粹功能方面轉變為將人類納入迴圈，以及通過理解和表達意圖來協調人類和機器人的行為。
16-848 指標：靈巧操作的設計和控制
教授： 南希波拉德
課程描述：
在過去十年中，與手相關的研究急劇增加。雙手是計算機圖形學和虛擬現實的焦點，新的機器人手已經大量湧現，並且在DARPA機器人挑戰賽等廣泛宣傳的專案中已經出現了操縱。掌握了所有這些注意力，我們是否接近靈巧的突破，或者我們是否仍然缺少真正稱職的操作所需的一些東西？在本課程中，我們將調查機器人手並瞭解人手，目標是推動手頭設計和控制靈巧操作的邊界。我們將考慮靈活性所需的運動學和動力學，進行靈巧相互作用需要哪些感測器，反射和順應性的重要性以及不確定性的挑戰。我們將研究人手：它的結構，感知能力，人類掌握選擇以及靈感和基準測試的控制策略。學生將被要求提交一篇或兩篇研究論文，參與討論和簡短的研究或設計練習，並進行最終的專案。
16-861 移動機器人開發
課程描述：
本課程研究機器人的移動性，能量學，感測，計算，軟體，有效載荷，介面和操作環境。背景是機器人對月球的追求。範圍包括機械，電子，軟體，運動，導航，通訊，感測，電源和散熱考慮因素。此外，空間系統還解決了低質量，能量，空間環境和設計可靠性的挑戰。媒體被納入編年史並代表成就。該課程適合廣泛的學生學科和興趣。課程學習目標包括制定，解決問題，機器人和開發空間系統。學生團隊合作，提供指導，通過書面和口頭報告製作與任務相關的結果並練習技術交流。團隊製作學期論文，詳細說明設計，開發，測試和經驗教訓。
16-865 高階移動機器人開發
課程描述：
本課程將開發CMU將在月球上駕駛的機器人，以獲得Lunar XPrize，同時指導支流技術和創作過程。該課程還將通過展示流動站的飛行準備情況，從谷歌的月球里程碑獎中獲得第一筆現金。本課程涉及的支流技術包括機制，驅動，熱調節，功率，感測，計算，通訊和操作。流程包括機器人開發和功能，可靠性和飛行準備情況的驗證。相關技能包括機器人，機械，電子，軟體，製造，測試，文件和系統工程。該課程適合廣泛的興趣和經驗。
16-868 生物力學和電機控制
教授： Hartmut Geyer
課程描述：
該課程介紹了腿部運動的機制和控制，重點是人體系統。涵蓋的主要議題包括基本概念，肌肉骨骼力學和神經控制。強調了機器人和康復裝置中的生物靈感的例子。在課程結束時，您將掌握基本知識，建立自己動態的動物和人類運動的控制模型。該課程同時開發材料，並介紹了Matlab的Simulink和SimMechanics環境，用於建模非線性動態系統。作業和團隊專案將讓您在理論和計算機模擬中將您的知識應用於動物和人體運動的問題。
本科課程
16-223 IDeATe：物理計算簡介
教授： Garth Zeglin
課程描述：
物理計算是指物理系統的設計和構建，它使用軟體和硬體的混合來感知和響應周圍的世界。此類系統將數字和物理過程融入玩具和小工具，動力雕塑，功能感應和評估工具，移動儀器，互動式可穿戴裝置等。這是一個基於專案的課程，涉及使用物理計算構思，設計和開發專案的所有方面：應用程式，工件，計算機輔助設計環境和物理原型設施。該課程圍繞一系列實踐動手練習進行組織，其中介紹了電路，嵌入式程式設計，感測器訊號處理，簡單機制，動作和基於時間的行為的基本原理。關鍵目標是直觀地瞭解資訊和能量如何在物理，電子和計算域之間移動以建立所需的行為。這些練習為協作專案提供了基礎，這些專案利用基本技能，並挑戰學生不僅要考慮如何製作東西，還要考慮我們設計的物件，以及為什麼製作是值得的。本課程是IDeATe門戶課程，用於進入IDeATe智慧環境或物理計算程式。CFA / DC / TPR學生可以在16-223歲之間註冊; CIT / MCS / SCS學生可以報名參加60-223版本的課程。請注意，本課程將包含實驗室使用和材料費用。和計算域來建立所需的行為。這些練習為協作專案提供了基礎，這些專案利用基本技能，並挑戰學生不僅要考慮如何製作東西，還要考慮我們設計的物件，以及為什麼製作是值得的。本課程是IDeATe門戶課程，用於進入IDeATe智慧環境或物理計算程式。CFA / DC / TPR學生可以在16-223歲之間註冊; CIT / MCS / SCS學生可以報名參加60-223版本的課程。請注意，本課程將包含實驗室使用和材料費用。和計算域來建立所需的行為。這些練習為協作專案提供了基礎，這些專案利用基本技能，並挑戰學生不僅要考慮如何製作東西，還要考慮我們設計的物件，以及為什麼製作是值得的。本課程是IDeATe門戶課程，用於進入IDeATe智慧環境或物理計算程式。CFA / DC / TPR學生可以在16-223歲之間註冊; CIT / MCS / SCS學生可以報名參加60-223版本的課程。請注意，本課程將包含實驗室使用和材料費用。但也為我們設計的物件，以及為什麼製作是值得的。本課程是IDeATe門戶課程，用於進入IDeATe智慧環境或物理計算程式。CFA / DC / TPR學生可以在16-223歲之間註冊; CIT / MCS / SCS學生可以報名參加60-223版本的課程。請注意，本課程將包含實驗室使用和材料費用。但也為我們設計的物件，以及為什麼製作是值得的。本課程是IDeATe門戶課程，用於進入IDeATe智慧環境或物理計算程式。CFA / DC / TPR學生可以在16-223歲之間註冊; CIT / MCS / SCS學生可以報名參加60-223版本的課程。請注意，本課程將包含實驗室使用和材料費用。
16-264 人型生物
教授： 克里斯阿特克森
課程描述：
本課程調查人類，類人機器人和人形圖形角色的感知，認知和運動。應用領域包括更像人類的機器人，視訊遊戲角色和互動式電影角色。
16-311 機器人簡介
教授： Howie Choset
課程描述：
本課程概述了實踐和研究中的機器人技術，主題包括視覺，運動規劃，移動機制，運動學，反向運動學和感測器。在課程專案中，學生構建由微控制器驅動的機器人，每個專案都強化了講座中開發的基本原則。學生名義上以三人一組的形式工作：電氣工程師，機械工程師和電腦科學家。本課程還將向學生介紹機器人技術的一些當代事件，其中包括當前機器人實驗室研究，應用，機器人競賽和新聞機器人。
16-350 機器人規劃技術
教授： Maxim Likhachev
課程描述：
規劃是使機器人自主的核心元件之一。機器人規劃負責實時決定機器人接下來應該做什麼，如何做，機器人在哪裡移動以及如何移動到那裡。本課程深入研究了機器人中的流行規劃技術，並研究了它們在地面和空中機器人，人形機器人，移動操縱平臺和多機器人系統中的應用。學生們學習這些方法的理論，並在一系列基於程式設計的專案中實現它們。要上課，學生應該參加機器人入門課程，並對程式設計和資料結構有很好的瞭解。
預備要求：
- 機器人技術簡介
16-362 移動機器人程式設計實驗室
教授： Alonzo Kelly
課程描述：
本課程全面介紹了使移動機器人可靠有效執行所需的概念和基本演算法。我們將與小型機器人合作，這些小型機器人可通過膝上型電腦進行無線控制。機器人是定製設計的迷你叉車，可以像現在的商用自動導引車一樣將托盤從一個地方移動到另一個地方。機器人在現代MATLAB程式設計環境中程式設計。它是一種非常容易學習的語言，也是一種非常強大的語言機器人演算法原型。除了一些理論，你將在這門課程中獲得很多經驗。講座主要關注下一個實驗的內容。每週都有一個實驗室，它們相互依賴，從而形成一個完整的機器人軟體系統。本課程將以全班級機器人比賽結束，該比賽將測試本學期實施的所有程式碼的效能。為了在課程中取得成功，學生必須具備1）科學/工程水平的數學背景（矩陣，向量，座標系）和2）已經掌握了至少一種程式式程式設計語言，如C或Java，以及3 ）有足夠的經驗，有合理的準備在13周內在一兩個人的幫助下編寫5000行軟體系統。課程結束後，您將編寫一個軟體系統，該系統已逐步擴充套件功能並在整個學期定期除錯。為了在課程中取得成功，學生必須具備1）科學/工程水平的數學背景（矩陣，向量，座標系）和2）已經掌握了至少一種程式式程式設計語言，如C或Java，以及3 ）有足夠的經驗，有合理的準備在13周內在一兩個人的幫助下編寫5000行軟體系統。課程結束後，您將編寫一個軟體系統，該系統已逐步擴充套件功能並在整個學期定期除錯。為了在課程中取得成功，學生必須具備1）科學/工程水平的數學背景（矩陣，向量，座標系）和2）已經掌握了至少一種程式式程式設計語言，如C或Java，以及3 ）有足夠的經驗，有合理的準備在13周內在一兩個人的幫助下編寫5000行軟體系統。課程結束後，您將編寫一個軟體系統，該系統已逐步擴充套件功能並在整個學期定期除錯。3）有足夠的經驗，有合理的準備，在一個或兩個人的幫助下，在13周內編寫5000線軟體系統。課程結束後，您將編寫一個軟體系統，該系統已逐步擴充套件功能並在整個學期定期除錯。3）有足夠的經驗，有合理的準備，在一個或兩個人的幫助下，在13周內編寫5000線軟體系統。課程結束後，您將編寫一個軟體系統，該系統已逐步擴充套件功能並在整個學期定期除錯。
16-374 IDeATe：機器人特效的藝術
教授： Garth Zeglin和Suzie Silver
課程描述：
靈感來自George Melies的早期技巧電影，這個以專案為導向的課程將機器人技術和電影製作技術融合在一起，為電影注入現場魔力的奇蹟。學生將學習使用電子動畫，相機運動控制和合成的電影製作的基礎知識。這些專案運用這些技術為短片創造創新的物理效果，從概念到後期製作。該課程強調實時實踐效果，以探索即興和排練的即時性和互動性。機器人主題包括電子動畫快速原型設計和程式設計人機協作效能。該課程包括特殊效果和機器人技術歷史的簡要概述，以便在上下文中設定工作。交叉上市課程：60428
16-375 IDeATe：創意實踐機器人
教授： Garth Zeglin
課程描述：
[IDeATe合作課程]。這個以專案為導向的課程將藝術和工程結合在一起，製造出驚人的動畫機器。學生將通過幾個小專案來重複他們的概念，這些專案專注於使用體現行為作為講故事，表演和人際互動的創造性媒介。學生將學習設計，構建和程式設計簡單機器人系統的技能，然後通過展覽和表演探索他們的成果。技術主題包括系統思考，動態物理和計算行為，自治，嵌入式程式設計以及製造和部署。討論主題包括當代動力學雕塑和機器人研究。請注意，本課程可能會產生使用/材料費用。交叉上課課程：
預備要求：
- 62150或60223或15104或16223
16-384 機器人運動學和動力學
教授： Howie Choset和George A. Kantor
課程描述：
機器人運動學的基礎和原理。主題包括變換，正向運動學，反向運動學，差分運動學（雅可比行列式），可操縱性和基本運動方程。課程還包括機器人手臂程式設計。
涵蓋的主題：
- 轉換
- 正向運動學
- 反向運動學
- 差動運動學（雅可比）
- 操作性
- 基本運動方程
16-385 計算機視覺
教授： Ioannis Gkioulekas和Kris M. Kitani
課程描述：
本課程全面介紹計算機視覺。主要議題包括影象處理，檢測和識別，基於幾何和基於物理的視覺，感測和感知以及視訊分析。學生將學習計算機視覺的基本概念以及實踐經驗，以解決現實生活中的視力問題。
16-423 設計計算機視覺應用程式
教授： Simon Lucey
課程描述：
教授：Simon Lucey學期：秋季和春季課程描述：計算機視覺是一門試圖從影象和視訊中提取資訊的學科。地球上幾乎每個智慧裝置都有一個攝像頭，人們越來越關注如何開發使用計算機視覺的應用程式來執行不斷擴充套件的事物列表，包括：3D對映，照片/影象搜尋，人/物體跟蹤，擴增實境本課程面向不熟悉計算機視覺但希望快速掌握最新環境，軟體工具和開發計算機視覺應用程式最佳實踐的學生。雖然必須具備強大的程式設計背景（至少熟悉C / C ++），但不需要先前的計算機視覺或機器學習知識。主題將包括使用傳統的計算機視覺軟體工具（OpenCV，MATLAB工具箱，VLFeat，CAFFE），以及使用移動視覺庫（如GPUImage）和快速數學庫（如Armadillo和Eigen）在iOS裝置上進行開發。為了保持一致性，所有應用程式開發都將在iOS中進行，並且預計參與該類的所有學生都可以訪問執行OS X Mavericks或更高版本的基於Intel的MAC。雖然課程將集中在一個作業系統上，但從本課程中獲得的知識旨在推廣到其他移動平臺，如Android等。為了保持一致性，所有應用程式開發都將在iOS中進行，並且預計參與該類的所有學生都可以訪問執行OS X Mavericks或更高版本的基於Intel的MAC。雖然課程將集中在一個作業系統上，但從本課程中獲得的知識旨在推廣到其他移動平臺，如Android等。為了保持一致性，所有應用程式開發都將在iOS中進行，並且預計參與該類的所有學生都可以訪問執行OS X Mavericks或更高版本的基於Intel的MAC。雖然課程將集中在一個作業系統上，但從本課程中獲得的知識旨在推廣到其他移動平臺，如Android等。
16-450 機器人系統工程
課程描述：
系統工程檢查指定，設計，分析和測試複雜系統的方法。在本課程中，介紹了系統工程的原理和過程，並將其應用於機器人裝置的開發。重點是設計用於執行復雜行為的機器人系統。這樣的系統嵌入計算元件，整合感測器和致動器，以可靠和穩健的方式操作，並且需要從概念到生產的嚴格工程。課程的組織是通過概念化，規範，設計和原型設計的系統工程過程，同時考慮驗證和驗證。完成本課程的學生將通過其競爭設計和初始原型設計機器人系統。
16-455 IDeATe：人機的虛擬性
教授： Garth Zeglin和Joshua Bard
課程描述：
[IDeATe]人類靈巧的技能體現了豐富的物理理解，補充了基於計算機的設計和機器製造。這個面向專案的課程通過創新設計和製造系統的實際開發探索了手與機器之間的二元性。這些系統將物理工具的表現力和直覺與數字領域的可擴充套件性和精確性相結合。學生將開發新穎的混合設計和生產工作流程，結合模擬和數字流程，以支援他們所選專案的設計和製造。涵蓋的具體技能包括3D掃描，3D建模（CAD），3D列印（增材製造），基於計算機的感測和人機互動設計。感興趣的領域包括建築，藝術和產品設計。
預備要求：
- 60223或16223
16-467 人機互動
教授： Henny Admoni
課程描述：
人機互動領域（HRI）正迅速成為機器人研究的重要領域。基本目標是在人與機器人之間創造自然而有效的互動。HRI是高度跨學科的，彙集了機器人技術，人工智慧，人機互動，心理學，教育和其他領域的方法和技術。本課程主要以講座為基礎，包括課堂參與式小型專案，家庭作業，一個小組學期專案，使學生能夠將理論付諸實踐，並進行最後的決定。所涉及的主題將包括支援人機互動的技術，人與機器人之間的互動心理，如何設計和開展HRI研究，以及輔助機器人等現實應用。
16-474 Robotics Capstone
教授： Cameron Riviere和Dimitrios（Dimi）Apostolopoulos
課程描述：
在本課程中，學生將完善設計，構建，整合，測試和演示他們在先修系統工程課程（16-450）中設計的機器人的效能。希望學生繼續應用系統工程的流程和方法來跟蹤需求，評估備選方案，優化網路物理架構，規劃和設計測試，驗證設計並驗證系統效能。此外，學生還學習並應用專案管理技術來管理專案的技術範圍，進度，預算和風險。該課程包括講座，課堂會議，評論和最終演示。講座涵蓋專案管理的核心主題和系統工程專題。在課堂會議期間，學生和教師審查專案進展並討論技術和專案執行方面的挑戰。大約在學期前三個月結束時有三個主要評論。對於每次稽核，學生都會進行演示並提交系統設計和開發文件的更新版本。該課程最後在學期結束時進行系統效能驗證演示。除此之外，學生還為更廣泛的機器人社群舉辦了機器人系統的特別演示。學生們進行演示並提交系統設計和開發文件的更新版本。該課程最後在學期結束時進行系統效能驗證演示。除此之外，學生還為更廣泛的機器人社群舉辦了機器人系統的特別演示。學生們進行演示並提交系統設計和開發文件的更新版本。該課程最後在學期結束時進行系統效能驗證演示。除此之外，學生還為更廣泛的機器人社群舉辦了機器人系統的特別演示。
16299 反饋控制系統簡介
教授： 內森邁克爾
課程描述：
本課程是電腦科學專業反饋控制系統的第一門課程。課程主題包括經典線性控制理論（微分方程，拉普拉斯變換，反饋控制），線性狀態空間方法（可控性/可觀測性，極點配置，LQR），非線性系統理論以及使用計算機學習技術進行控制的介紹。電腦科學專業將優先考慮機器人技術。
預備要求：
- 15122
- 21122
原文如下：
GRADUATE COURSES
16-623 Advanced Computer Vision Apps
Professor: Simon Lucey
Course Description:
Computer vision is a discipline that attempts to extract information from images and videos. Nearly every smart device on the planet has a camera, and people are increasingly interested in how to develop apps that use computer vision to perform an ever expanding list of things including: 3D mapping, photo/image search, people/object tracking, augmented reality etc. This course is intended for graduate students who are familiar with computer vision, and are keen to learn more about the applying state of the art vision methods on smart devices and embedded systems. A strong programming background is a must (at a minimum good knowledge of C/C++), topics will include using conventional computer vision software tools (OpenCV, MATLAB toolboxes, VLFeat, CAFFE, Torch 7), and development on iOS devices using mobile vision libraries such as GPUImage, Metal and fast math libraries like Armadillo and Eigen. For consistency, all app development will be in iOS and it is expected that all students participating in the class have access to an Intel-based MAC running OS X Mavericks or later. Although the coursework will be focused on a single operating system, the knowledge gained from this class will easily generalize to other mobile platforms such as Android etc.
16-642 Manipulation, Estimation, and Control
Professor: George A. Kantor
Course Description:
This course provides an overview of the current techniques that allow robots to locomote and interact with the world. The kinematics and dynamics of electromechanical systems will be covered with a particular focus on their application to robotic arms. Some basic principles of robot control will be discussed, ranging from independent- joint PID tracking to coupled computed torque approaches. The practice and theory of robotic mobility will be investigated through various mobile robot platforms, including wheeled and tracked vehicle and legged robots. Hands-on experience with some of the topics in the class will be provided through practical demonstrations and lab assignments. Please note that this course is for MRSD students only.
Prerequisites:
- MRSD students only
16-650 Systems Engineering & Management for Robotics
Professor: Dimitrios (Dimi) Apostolopoulos
Course Description:
Practically everything around us is a system-from the cell phone in your pocket to the International Space System up in the sky. The higher the complexity of the system, the more its creators benefit from applying formal processes to its development-processes that are collectively known under the umbrella “systems engineering.” Systems Engineering is a formal discipline that guides a product from conception and design all the way to production, marketing, servicing, and disposal. In this course we will study the fundamental elements of systems engineering as they apply to the development of robotic systems. We will cover topics such as needs analysis, requirements elicitation and formalization, system architecture development, trade studies, verification and validation, etc. In addition, for this course we will cover core topics of Project Management that must be performed in tandem with Systems Engineering to achieve a successful project and product. For the Project Management we will cover work breakdown structures, scheduling, estimation, and risk management. We will study both classical and agile methods in project management. The students will apply most of the elements of this course in the MRSD Project Course I and II, thus giving them the opportunity to put the theory in practice in a real product design activity. Please note that this course is for MRSD students only. (Past project examples: http://mrsd.ri.cmu.edu/project-examples/)
16-662 Robot Autonomy
Professor: Oliver Kroemer
Course Description:
Robot autonomy delves into the interplay between perception, manipulation, navigation, planning, and learning required to develop fully autonomous systems. We will focus on application domains like the home, retail, and healthcare and identify common themes and key bottlenecks. We will discuss the state of the art algorithms, their computational and hardware requirements, and their limitations. To enable you to create end-to-end systems, you will learn how to address clutter and uncertainty in manipulation tasks, develop robust object recognition algorithms in real-world scenes, plan robot trajectories in high-dimensional spaces, build behavior engines for high-level tasks, and learn to apply and connect those to create an autonomous robot system. The course emphasizes the implementation of the algorithms discussed in class in simulation through homework assignments as well as on real systems in a class project.
Prerequisites:
- 16-450
16-711 Kinematics, Dynamic Systems and Control
Professor: Hartmut Geyer and Chris Atkeson
Course Description:
Kinematics, Dynamic Systems, and Control is a graduate level introduction to robotics. The course covers fundamental concepts and methods to analyze, model and control robotic mechanisms which move in the physical world and manipulate it. Main topics include the fundamentals of kinematics, dynamics and control applied to the kinematics, dynamics and control of rigid body chains. Additional topics include state estimation and dynamic parameter identification.
16-720 Computer Vision
Professor: Kris M. Kitani, Srinivasa G. Narasimhan, Simon Lucey, Deva Kannan Ramanan, Yaser Ajmal Sheikh, Abhinav Gupta and Martial Hebert
Course Description:
This course introduces the fundamental techniques used in computer vision, that is, the analysis of patterns in visual images to reconstruct and understand the objects and scenes that generated them. Topics covered include image formation and representation, camera geometry, and calibration, computational imaging, multi-view geometry, stereo, 3D reconstruction from images, motion analysis, physics-based vision, image segmentation and object recognition. The material is based on graduate-level texts augmented with research papers, as appropriate. Evaluation is based on homeworks and a final project. The homeworks involve considerable Matlab programming exercises.
Books:
- Richard Szeliski, "Computer Vision Algorithms and Applications", Texts in Computer Science, Springer ISBN: 978-1-84882-934-3, Book Requirement: Recommended
- David Forsyth and Jean Ponce, "Computer Vision: A Modern Approach", Prentice Hall ISBN: 0-13-085198-1, Book Requirement: Recommended
16-722 Sensing & Sensors
Professor: Cameron Riviere and Mel Siegel
Course Description:
The principles and practices of quantitative perception (sensing) illustrated by the devices and algorithms (sensors) that implement them. Learn to critically examine the sensing requirements of robotics applications, to specify the required sensor characteristics, to analyze whether these specifications can be realized even in principle, to compare what can be realized in principle to what can actually be purchased or built, to understand the engineering factors that account for the discrepancies, and to design transducing, digitizing, and computing systems that come tolerably close to realizing the actual capabilities of available sensors. Grading will be based on homework assignments, class participation, and a final exam. Three or four of the homework assignments will be hands-on “take-home labs” done with an Arduino kit that students will purchase in lieu of purchasing a textbook. Top-level course modules will cover (1) sensors, signals, and measurement science, (2) origins, nature, and amelioration of noise, (3) end-to-end sensing systems, (4) cameras and other imaging sensors and systems, (5) range sensing and imaging, (6) navigation sensors and systems, (7) other topics of interest to the class (as time allows).
16-725 Medical Image Analysis
Professor: John Galeotti
Course Description:
Students will gain theoretical and practical skills in 2D, 3D, and 4D biomedical image analysis, including skills relevant to general image analysis. The fundamentals of computational medical image analysis will be explored, leading to current research in applying geometry and statistics to segmentation, registration, visualization, and image understanding. Additional and related covered topics include de-noising/restoration, morphology, level sets, and shape/feature analysis. Students will develop practical experience through projects using the latest version of the National Library of Medicine Insight Toolkit ( ITK ) and SimpleITK, a popular open-source software library developed by a consortium of institutions including Carnegie Mellon University and the University of Pittsburgh. In addition to image analysis, the course will include interaction with radiologists and pathologist(s). *** Lectures are at CMU and students will visit clinicians at UPMC. Some or all of the class lectures may also be videoed for public distribution, but students may request to be excluded from distributed video. 16-725 is a graduate class, and 16-425 is a cross-listed undergraduate section. 16-425 has substantially reduced requirements for the final project and for the larger homework assignments, nor does it require shadowing the clinicians.
Prerequisites:
- Knowledge of vector calculus, basic probability, and either C++ or python, including basic command-line familiarity and how to pass arguments to your own command-line programs. Extensive expertise with C++ and templates is not necessary, but some students may find it helpful.
16-741 Mechanics of Manipulation
Professor: Matthew T. Mason
Course Description:
Kinematics, statics, and dynamics of robotic manipulator’s interaction with a task, focusing on intelligent use of kinematic constraint, gravity, and frictional forces. Automatic planning based on mechanics. Application examples drawn from manufacturing and other domains.
16-745 Dynamic Optimization
Professor: Chris Atkeson
Course Description:
This course surveys the use of optimization (especially optimal control) to design behavior. We will explore ways to represent policies including hand-designed parametric functions, basis functions, tables, and trajectory libraries. We will also explore algorithms to create policies including parameter optimization and trajectory optimization (first and second order gradient methods, sequential quadratic programming, random search methods, evolutionary algorithms, etc.). We will discuss how to handle the discrepancy between models used to create policies and the actual system being controlled (evaluation and robustness issues). The course will combine lectures, student-presented material, and projects. The goal of this course will be to help participants find the most effective methods for their problems.
16-761 Introduction to Mobile Robots
Professor: Alonzo Kelly
Course Description:
This course covers all aspects of mobile robot systems design and programming from both a theoretical and a practical perspective. The basic subsystems of control, localization, mapping, perception, and planning are presented. For each, the discussion will include relevant methods from applied mathematics. aspects of physics necessary in the construction of models of system and environmental behavior, and core algorithms which have proven to be valuable in a wide range of circumstances.
16-778 Mechatronic Design
Professor: John M. Dolan
Course Description:
Mechatronics is the synergistic integration of mechanism, electronics, and computer control to achieve a functional system. This course is a semester-long multidisciplinary capstone hardware project design experience in which small (typically four-person) teams of electrical and computer engineering, mechanical engineering and robotics students deliver an end-of-course demonstration of a final integrated system capable of performing a mechatronic task. Throughout the semester, the students design, configure, implement, test and evaluate in the laboratory devices and subsystems culminating in the final integrated mechatronic system. Lectures will complement the laboratory experience with comparative surveys, operational principles, and integrated design issues associated with the spectrum of mechanism, microcontroller, electronic, sensor, and control components. CROSS-LISTED COURSES: 18-578, 24-778
16-782 Planning and Decision-making in Robotics
Professor: Maxim Likhachev
Course Description:
Planning and Decision-making are critical components of autonomy in robotic systems. These components are responsible for making decisions that range from path planning and motion planning to coverage and task planning to taking actions that help robots understand the world around them better. This course studies underlying algorithmic techniques used for planning and decision-making in robotics and examines case studies in ground and aerial robots, humanoids, mobile manipulation platforms and multi-robot systems. The students will learn the algorithms and implement them in a series of programming-based projects.
16-785 Integrated intelligence in robotics: language, vision and planning
Professor: Jean Hyaejin Oh
Course Description:
This course covers the topics on building cognitive intelligence for robotic systems. Cognitive capabilities constitute high-level, humanlike intelligence that exhibits reasoning or problem solving skills. Such capabilities as semantic perception, language understanding, and task planning can be built on top of low-level robot autonomy that enables autonomous control of physical platforms. The topics generally bridge across multiple technical areas, for example, vision-language intersection and language-action/plan grounding. This course is composed of 50 lectures and 50 seminar classes.
Prerequisites:
- There are no explicit prerequisites for this class, but a general background knowledge in AI and machine learning is assumed.
16-791 Applied Data Science
Professor: Artur W. Dubrawski
Course Description:
This course explores the rapidly developing field of data science in the context of its pragmatic applications. Applied Data Science strives to achieve three main goals. The first is to optimize the efficacy of decision making by human managers. The second is to maximize the utilization of available data, so that no important clue is ever missed. The third is to improve understanding of data and the underlying processes that produce it. This course aims at building skills required to systematically achieve those goals in practice. The students will gain and solidify awareness of the most prevalent contemporary methods of Data Science, and develop intuition needed for assessing practical utility of the studied topics in application scenarios. They will be able to learn how to formulate analytic tasks in support of project objectives, how to define successful analytic projects, and how to evaluate utility of existing and potential applications of the discussed technologies in practice.
16-811 Math Fundamentals for Robotics
Professor: Michael Erdmann
Course Description:
This course covers selected topics in applied mathematics useful in robotics, taken from the following list: 1. Solution of Linear Equations. 2. Polynomial Interpolation and Approximation. 3. Solution of Nonlinear Equations. 4. Roots of Polynomials, Resultants. 5. Approximation by Orthogonal Functions (includes Fourier series). 6. Integration of Ordinary Differential Equations. 7. Optimization. 8. Calculus of Variations (with applications to Mechanics). 9. Probability and Stochastic Processes (Markov chains). 10. Computational Geometry. 11. Differential Geometry.
16-822 Geometry Based Methods in Computer Vision
Professor: Martial Hebert
Course Description:
The course focuses on the geometric aspects of computer vision: The geometry of image formation and its use for 3D reconstruction and calibration. The objective of the course is to introduce the formal tools and results that are necessary for developing multi-view reconstruction algorithms. The fundamental tools introduced study affine and projective geometry, which are essential to the development of image formation models. Additional algebraic tools, such as exterior algebras are also introduced at the beginning of the course. These tools are then used to develop formal models of geometric image formation for a single view (camera model), two views (fundamental matrix), and three views (trifocal tensor); 3D reconstruction from multiple images; and auto-calibration.
Prerequisites:
- Computer Vision (16-721 or equivalent)
Books:
- The Geometry of Multiple Images. Faugeras and Long, MIT Press., Book Requirement: Not Specified
- Multiple View Geometry in Computer Vision, Richard Hartley and Andrew Zisserman, Cambridge University Press, June 2000., Book Requirement: Not Specified
Topics Covered:
- Fundamentals of projective, affine, and Euclidean geometries
- Invariance and duality
- Algebraic tools
- Single view geometry: The pinhole model
- Calibration techniques
- 2-view geometry: The Fundamental matrix
- 2-view reconstruction
- 3-view geometry: The trifocal tensor
- Parameter estimation and uncertainty
- n-view reconstruction
- Self-calibration
16-823 Physics Based Methods in Vision
Professor: Srinivasa G. Narasimhan
Course Description:
Everyday we observe an extraordinary array of light and color phenomena around us, ranging from the dazzling effects of the atmosphere, the complex appearances of surfaces and materials and underwater scenarios. For a long time, artists, scientists and photographers have been fascinated by these effects, and have focused their attention on capturing and understanding these phenomena. In this course, we take a computational approach to modeling and analyzing these phenomena, which we collectively call as “visual appearance”. The first half of the course focuses on the physical fundamentals of visual appearance, while the second half of the course focuses on algorithms and applications in a variety of fields such as computer vision, graphics and remote sensing and technologies such as underwater and aerial imaging. This course unifies concepts usually learnt in physical sciences and their application in imaging sciences, and will include the latest research advances in this area. The course will also include a photography competition.
Prerequisites:
- An undergraduate or graduate class in Computer Vision or in Computer Graphics
16-824 Learning-based Methods in Vision
Professor: Abhinav Gupta and Martial Hebert
Course Description:
A graduate seminar course in Computer Vision with emphasis on representation and reasoning for large amounts of data (images, videos and associated tags, text, gps-locations etc) toward the ultimate goal of Image Understanding. We will be reading an eclectic mix of classic and recent papers on topics including: Theories of Perception, Mid-level Vision (Grouping, Segmentation, Poselets), Object and Scene Recognition, 3D Scene Understanding, Action Recognition, Contextual Reasoning, Image Parsing, Joint Language and Vision Models, etc. We will be covering a wide range of supervised, semi-supervised and unsupervised approaches for each of the topics above.
Prerequisites:
- Graduate Computer Vision or Machine Learning course
16-831 Statistical Techniques in Robotics
Professor: David Held, Kris M. Kitani, Michael Kaess and J. Andrew (Drew) Bagnell
Course Description:
Data-driven learning techniques are now an essential part of building robotic systems designed to operate in the real world. These systems must learn to adapt to changes in the environment, learn from experience, and learn from demonstration. In particular we will cover three important sub-fields of Machine Learning applied to robotic systems: (1) We will cover Online Learning, which can be used to give robotic systems the ability to adapt to changing environmental conditions. (2) We will cover Reinforcement Learning, which takes into account the tradeoffs between exploration and exploitation to learn how to interact with the environment. We will also cover Deep Reinforcement Learning techniques in the context of real-world robotic systems. (3) We will cover Apprenticeship Learning (Imitation Learning and Inverse Reinforcement Learning) which is critical for teaching robotic systems to learn from expert behavior. Prerequisites: Linear Algebra, Multivariate Calculus, Probability theory.
16-833 Robot Localization & Mapping
Professor: Michael Kaess
Course Description:
Robot localization and mapping are fundamental capabilities for mobile robots operating in the real world. Even more challenging than these individual problems is their combination: simultaneous localization and mapping (SLAM). Robust and scalable solutions are needed that can handle the uncertainty inherent in sensor measurements, while providing localization and map estimates in real-time. We will explore suitable efficient probabilistic inference algorithms at the intersection of linear algebra and probabilistic graphical models. We will also explore state-of-the-art systems.
Topics Covered:
- simultaneous localization and mapping (SLAM)
16-843 Manipulation Algorithms
Professor: Katharina Muelling
Course Description:
This is an advanced graduate-level class on the theory and algorithms that enable robots to physically manipulate their world, on their own or in collaboration with people. The class will first focus on functional aspects of manipulation, such as synthesizing robust and stable grasps for dexterous hands and motion planning in these spaces, as well as learning for manipulation, such as how to predict stable grasps from demonstration and experience. Moving forward, we will discuss additional requirements that arise from performing manipulation tasks collaboratively with people: moving from purely functional aspects of motion to incorporating the human into the loop, and coordinating human and robot actions via understanding and expressing intent.
16-848 Hands: Design and Control for Dexterous Manipulation
Professor: Nancy Pollard
Course Description:
Research related to hands has increased dramatically over the past decade. Hands are in focus in computer graphics and virtual reality, new robot hands have been popping up in great variety, and manipulation has been featured in widely publicized programs such as the DARPA Robotics Challenge. With all of this attention on hands, are we close to a breakthrough in dexterity, or are we still missing some things needed for truly competent manipulation? In this course, we will survey robotic hands and learn about the human hand with the goal of pushing the frontiers on hand design and control for dexterous manipulation. We will consider the necessary kinematics and dynamics for dexterity, what sensors are required to carry out dexterous interactions, the importance of reflexes and compliance, and the challenge of uncertainty. We will examine the human hand: its structure, sensing capabilities, human grasp choice and control strategies for inspiration and benchmarking. Students will be asked to present one or two research papers, participate in discussions and short research or design exercises, and carry out a final project.
16-861 Mobile Robot Development
Professor: William (Red) L. Whittaker
Course Description:
This course investigates robot mobility, energetics, sensing, computing, software, payload, interface, and operating environment. The context is robotic pursuit of the Moon. Scope incorporates mechanism, electronics, software, locomotion, navigation, communication, sensing, power and thermal considerations. Additionally, space systems address challenges of low mass, energetics, space environment, and reliability of design. Media is incorporated to chronicle and represent the accomplishments. The course is appropriate for a broad range of student disciplines and interests. Course Learning Objectives include formulation, problem solving, robotics and developing space systems . Students work cooperatively in teams with guidance to produce mission-relevant results and practice technical communications through written and oral presentations. Teams generate term papers detailing the design, development, testing and lessons learned.
16-865 Advanced Mobile Robot Development
Professor: William (Red) L. Whittaker
Course Description:
This course will develop the robot that CMU will drive on the moon to win the Lunar XPrize while mentoring the tributary technologies and creative process. The course will also claim the first cash from Google’s Lunar Milestone Prize by demonstrating flight readiness of the rover. The tributary technologies addressed in this course include mechanisms, actuation, thermal regulation, power, sensing, computing, communication, and operations. Process includes robot development and verification of functionality, reliability, and flight readiness. Relevant skills include robotics, mechanics, electronics, software, fabrication, testing, documentation, and systems engineering. The course is appropriate for a broad range of interests and experience.
16-868 Biomechanics and Motor Control
Professor: Hartmut Geyer
Course Description:
The course provides an introduction into the mechanics and control of legged locomotion with a focus on the human system. The main topics covered include fundamental concepts, muscle-skeleton mechanics, and neural control. Examples of bio-inspiration in robots and rehabilitation devices are highlighted. By the end of the course, you will have the basic knowledge to build your own dynamic an control models of animal and human motions. The course develops the material in parallel with an introduction into Matlab’s Simulink and SimMechanics environments for modeling nonlinear dynamic systems. Assignments and team projects will let you apply your knowledge to problems of animal and human motion in theory and computer simulations.
UNDERGRADUATE COURSES
16-223 IDeATe: Introduction to Physical Computing
Professor: Garth Zeglin
Course Description:
Physical computing refers to the design and construction of physical systems that use a mix of software and hardware to sense and respond to the surrounding world. Such systems blend digital and physical processes into toys and gadgets, kinetic sculpture, functional sensing and assessment tools, mobile instruments, interactive wearables, and more. This is a project-based course that deals with all aspects of conceiving, designing and developing projects with physical computing: the application, the artifact, the computer-aided design environment, and the physical prototyping facilities. The course is organized around a series of practical hands-on exercises which introduce the fundamentals of circuits, embedded programming, sensor signal processing, simple mechanisms, actuation, and time-based behavior. The key objective is gaining an intuitive understanding of how information and energy move between the physical, electronic, and computational domains to create a desired behavior. The exercises provide building blocks for collaborative projects which utilize the essential skills and challenge students to not only consider how to make things, but also for whom we design, and why the making is worthwhile. This course is an IDeATe Portal Course for entry into either of the IDeATe Intelligent Environments or Physical Computing programs. CFA/DC/TPR students can enroll under 16-223; CIT/MCS/SCS students can enroll in the 60-223 version of the course. Please note that there will be lab usage and materials fees associated with this course.
16-264 Humanoids
Professor: Chris Atkeson
Course Description:
This course surveys perception, cognition, and movement in humans, humanoid robots, and humanoid graphical characters. Application areas include more human-like robots, video game characters, and interactive movie characters.
16-311 Introduction to Robotics
Professor: Howie Choset
Course Description:
This course presents an overview of robotics in practice and research with topics including vision, motion planning, mobile mechanisms, kinematics, inverse kinematics, and sensors. In course projects, students construct robots which are driven by a microcontroller, with each project reinforcing the basic principles developed in lectures. Students nominally work in teams of three: an electrical engineer, a mechanical engineer, and a computer scientist. This course will also expose students to some of the contemporary happenings in robotics, which includes current robot lab research, applications, robot contests and robots in the news.
16-350 Planning Techniques for Robots
Professor: Maxim Likhachev
Course Description:
Planning is one of the core components that enable robots to be autonomous. Robot planning is responsible for deciding in real-time what should the robot do next, how to do it, where should the robot move next and how to move there. This class does an in-depth study of popular planning techniques in robotics and examines their use in ground and aerial robots, humanoids, mobile manipulation platforms and multi-robot systems. The students learn the theory of these methods and also implement them in a series of programming-based projects. To take the class students should have taken an Intro to Robotics class and have a good knowledge of programming and data structures.
Prerequisites:
- Introduction to Robotics
16-362 Mobile Robot Programming Laboratory
Professor: Alonzo Kelly
Course Description:
This course is a comprehensive hands-on introduction to the concepts and basic algorithms needed to make a mobile robot function reliably and effectively. We will work in small groups with small robots that are controlled over wireless from your laptop computers. The robots are custom-designed mini forktrucks that can move pallets from place to place just like commercial automated guided vehicles do today. The robots are programmed in the modern MATLAB programming environment. It is a pretty easy language to learn, and a very powerful one for prototyping robotics algorithms. You will get a lot of experience in this course in addition to some theory. Lectures are focused on the content of the next lab. There is a lab every week and they build on each other so that a complete robot software system results. The course will culminate with a class-wide robot competition that tests the performance of all of your code implemented in the semester. In order to succeed in the course, students must have a 1) 2nd year science/engineering level background in mathematics (matrices, vectors, coordinate systems) and 2) have already mastered at least one procedural programming language like C or Java, and 3) have enough experience to be reasonably prepared to write a 5000 line software system in 13 weeks with the help of one or two others. When the course is over, you will have written a single software system that has been incrementally extended in functionality and regularly debugged throughout the semester.
16-374 IDeATe: Art of Robotic Special Effects
Professor: Garth Zeglin and Suzie Silver
Course Description:
Inspired by the early trick films of George Melies, this project-oriented course brings together robotics and film production technique to infuse cinema with the wonder of live magic. Students will learn the basics of film production using animatronics, camera motion control, and compositing. The projects apply these techniques to create innovative physical effects for short films, all the way from concept to post-production. The course emphasizes real-time practical effects to explore the immediacy and interactivity of improvisation and rehearsal. The robotics topics include animatronic rapid prototyping and programming human-robot collaborative performance. The course includes a brief overview of the history of special effects and robotics to set the work in context. CROSS-LISTED COURSE: 60428
16-375 IDeATe: Robotics for Creative Practice
Professor: Garth Zeglin
Course Description:
[IDeATe collaborative course]. This project-oriented course brings art and engineering together into making machines which are surprisingly animate. Students will iterate their concepts through several small projects focused on using embodied behavior as a creative medium for storytelling, performance, and human interaction. Students will learn skills for designing, constructing and programming simple robot systems, then exploring their results through exhibition and performance. Technical topics include systems thinking, dynamic physical and computational behavior, autonomy, embedded programming, and fabrication and deployment. Discussion topics include both contemporary kinetic sculpture and robotics research. Please note that there may be usage/materials fees associated with this course. CROSS-LISTED COURSES: 54375
Prerequisites:
- 62150 or 60223 or 15104 or 16223
16-384 Robot Kinematics and Dynamics
Professor: Howie Choset and George A. Kantor
Course Description:
Foundations and principles of robotic kinematics. Topics include transformations, forward kinematics, inverse kinematics, differential kinematics (Jacobians), manipulability, and basic equations of motion. Course also include programming on robot arms.
Topics Covered:
- transformations
- forward kinematics
- inverse kinematics
- differential kinematics (Jacobians)
- manipulability
- basic equations of motion
16-385 Computer Vision
Professor: Ioannis Gkioulekas and Kris M. Kitani
Course Description:
This course provides a comprehensive introduction to computer vision. Major topics include image processing, detection and recognition, geometry-based and physics-based vision, sensing and perception, and video analysis. Students will learn basic concepts of computer vision as well as hands on experience to solve real-life vision problems.
16-423 Designing Computer Vision Apps
Professor: Simon Lucey
Course Description:
Professor: Simon Lucey Semester: Fall and Spring Course Description: Computer vision is a discipline that attempts to extract information from images and videos. Nearly every smart device on the planet has a camera, and people are increasingly interested in how to develop apps that use computer vision to perform an ever expanding list of things including: 3D mapping, photo/image search, people/object tracking, augmented reality etc. This course is intended for students who are not familiar with computer vision, but want to come up to speed rapidly with the latest in environments, software tools and best practices for developing computer vision apps. No prior knowledge of computer vision or machine learning is required although a strong programming background is a must (at a minimum good knowledge of C/C++). Topics will include using conventional computer vision software tools (OpenCV, MATLAB toolboxes, VLFeat, CAFFE), and development on iOS devices using mobile vision libraries such as GPUImage and fast math libraries like Armadillo and Eigen. For consistency, all app development will be in iOS and it is expected that all students participating in the class have access to an Intel-based MAC running OS X Mavericks or later. Although the coursework will be focussed on a single operating system, the knowledge gained from this class is intended to generalize to other mobile platforms such as Android etc.
16-450 Robotics Systems Engineering
Professor: David Wettergreen
Course Description:
Systems engineering examines methods of specifying, designing, analyzing and testing complex systems. In this course, principles and processes of systems engineering are introduced and applied to the development of robotic devices. The focus is on robotic system engineered to perform complex behavior. Such systems embed computing elements, integrate sensors and actuators, operate in a reliable and robust fashion, and demand rigorous engineering from conception through production. The course is organized as a progression through the systems engineering process of conceptualization, specification, design, and prototyping with consideration of verification and validation. Students completing this course will engineer a robotic system through its compete design and initial prototype. The project concept and teams can continue into the Spring-semester (16-474 Robotics Capstone) for system refinement, testing and demonstration.
16-455 IDeATe: Human-Machine Virtuosity
Professor: Garth Zeglin and Joshua Bard
Course Description:
[IDeATe course] Human dexterous skill embodies a wealth of physical understanding which complements computer-based design and machine fabrication. This project-oriented course explores the duality between hand and machine through the practical development of innovative design and fabrication systems. These systems fluidly combine the expressivity and intuition of physical tools with the scalability and precision of the digital realm. Students will develop novel hybrid design and production workflows combining analog and digital processes to support the design and fabrication of their chosen projects. Specific skills covered include 3D scanning, 3D modeling (CAD), 3D printing (additive manufacturing), computer based sensing, and human-robot interaction design. Areas of interest include architecture, art, and product design. CROSS-LISTED COURSE: 48530
Prerequisites:
- 60223 or 16223
16-467 Human Robot Interaction
Professor: Henny Admoni
Course Description:
The field of human-robot interaction (HRI) is fast becoming a significant area of research in robotics. The basic objective is to create natural and effective interactions between people and robots. HRI is highly interdisciplinary, bringing together methodologies and techniques from robotics, artificial intelligence, human-computer interaction, psychology, education, and other fields. This course is primarily lecture-based, with in-class participatory mini-projects, homework assignments, a group term project that will enable students to put theory to practice, and a final. The topics covered will include technologies that enable human-robot interactions, the psychology of interaction between people and robots, how to design and conduct HRI studies, and real-world applications such as assistive robots. This course has no prerequisites, but some basic familiarity with robots is recommended (programming knowledge is not necessary, but is useful for the term project).
16-474 Robotics Capstone
Professor: Cameron Riviere and Dimitrios (Dimi) Apostolopoulos
Course Description:
In this course students refine the design, build, integrate, test, and demonstrate the performance of the robot they designed in the pre-requisite Systems Engineering Course (16-450). The students are expected to continue to apply the process and methods of Systems Engineering to track requirements, evaluate alternatives, refine the cyberphysical architectures, plan and devise tests, verify the design, and validate system performance. In addition, the students learn and apply Project Management techniques to manage the technical scope, schedule, budget, and risks of their project. The course consists of lectures, class meetings, reviews, and a final demonstration. Lectures cover core topics in Project Management and special topics in Systems Engineering. During class meetings the students and instructor review progress on the project and discuss technical and project-execution challenges. There are three major reviews approximately at the end of each of the first three months of the semester. For each review, the students give a presentation and submit an updated version of the System Design and Development Document. The course culminates in a System Performance Validation Demonstration at the end of the semester. In addition to that the students hold a special demonstration of their robotic system for the broader Robotics community.
16299 Introduction to Feedback Control Systems
Professor: Nathan Michael
Course Description:
This course is designed as a first course in feedback control systems for computer science majors. Course topics include classical linear control theory (differential equations, Laplace transforms, feedback control), linear state-space methods (controllability/observability, pole placement, LQR), nonlinear systems theory, and an introduction to control using computer learning techniques. Priorities will be given to computer science majors with robotics minor.
Prerequisites:
- 15122
- 21122
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