Teaching‎ > ‎

RI 16-843: Manipulation Algorithms


Katharina Muelling (kmuelling -- nrec.ri.cmu.edu) 

Stefanos Nikolaidis (snikolai -- cmu.edu)


Tue/Th, 9:00 am -- 10:20 am, NSH 3002


In this advanced graduate-level class, you will learn about the theory and algorithms that enable robots to physically
manipulate their world with and around people. We will first focus on functional aspects of manipulation, such as
synthesizing robust and stable grasps for dexterous hands, the geometry of manipulation configuration spaces, and
motion planning in these spaces. We will discuss both analytical and machine learning approaches. We will then
generalize these techniques to settings where robots manipulate objects together and in coordination with people. By 
the end of this class, you will be able to describe and compare algorithms for real-world manipulation, design user
studies to evaluate these algorithms in robot interactions with people and communicate your ideas to a peer audience.
Evaluation is based on student presentations, a final project and short weekly quizzes based on the assigned reading 

Paper Reviews

Please register for the paper review sessions. Please don't use this doodle as your preferred day of presenting, but as letting us 
know your availability. Only do not check a day if you are not available that day. 

Link to doodle


 Day     Date      Topic Reading  Notes
 Tue Aug 29  Introduction    Slides
 Thu Aug 31  Robotics Foundation    Slides
 Tue Sep 5  Grasping: Analytica Models Multi-fingered Hand Kinematics
(Chapter 5)
 Thu Sep 7  Grasping: Analytical and Data Driven  Models  Bohg et al., Data-Driven Grasp Synthesis - A Survey  Slides
 Tue Sep 12  Paper Review  Kevin: Automaticc Grasp Planning using Shape Primitives, Miller et al.
 Arpit: The YCB Object Model Set, Calli et al. 
 Thu Sep 14  Grasping: Data Driven Models
 Configuration Spaces
 Tue Sep 19  Paper Review  MattSupersizing Self-supervision: Learning to Graspfrom 50K Tries and 700 Robot Hours, Pinto and Gupta
 ChristopherGrasp Synthesis in Cluttered Environments for Dexterous Hands, Berenson et al. 
 Thu Sep 21  Configuration Spaces  Planning Algorithms, Chapter 4.3, LaValle
 Tue Sep 26  Paper Review  Jonathan: A simple motion planning algorithm for general robot manipulation, T. Lozano-Perez  Projects
 Thu Sep 28  Experimental Design
 Tue Oct 3  Paper Review      RobertoComparative Performance of Human and Mobile RoboticAssistants in Collaborative Fetch-and-Deliver Tasks, Unhelkar et al. 
Chi: Toward a Science of Robotics:Goals and Standards for Experimental Research, Takayama
 Thu Oct 5  Motion Planning: Combinatorial Planning    Slides
 Tue Oct 10  Paper Review  AdithyaPlanning Motions with Intentions, Koga et al. 
 KevinLearning Hand-Eye Coordination for Robotic Grasping with Deep Learningand Large-Scale Data Collection, Levine et al. 
 Thu Oct 12  Motion Planning: Sampling Based Planning    Slides
 Tue Oct 17  Paper Review  Arpit: Manipulation Planning Among Movable Obstacles; Stilman et al. 
 ChristopherVisibility-based probabilistic roadmapsfor motion planning, Simeon et al. 
 Thu Oct 19  Motion Planning: Sampling Based Planning, Local Planning, Alternative Motion Planning
 Tue Oct 24  Paper Review   JohnathanAnytime RRTs, Ferguson et al. 
 MattPlaying Catch and Jugglingwith a Humanoid Robot, Kober et al. 
 Thu Oct 26      
 Tue Oct 31      
 Thu Nov 2      
 Tue Nov 7      
 Thu Nov 9       
 Tue Nov 14      
 Thu Nov 16       
 Tue Nov 21       
 Thu   Nov 23  Thanksgiving    
 Tue   Nov 28      
 Thu Nov 30       
 Tue Dec 5       
 Thu Dec 7