CS 287H: Algorithmic HRI

Welcome to CS 287H Algorithmic Foundations of Human-Robot (and Human-AI) Interaction, Spring 2023!

Instructor: Anca Dragan (anca at berkeley dot edu)

GSI: Cassidy Laidlaw (cassidy_laidlaw at berkeley dot edu)

Lectures: TuTh, 2-3:30pm, Soda 310


As robot autonomy advances, it becomes more and more important to develop algorithms that are not solely functional, but also mindful of the end-user. How should the robot move differently when it's moving in the presence of a human? How should it learn from user feedback? How should it assist the user in accomplishing day to day tasks? These are the questions we will investigate in this course.

We will contrast existing algorithms in robotics with studies in human-robot interaction, discussing how to tackle interaction challenges in an algorithmic way, with the goal of enabling generalization across robots and tasks. We will also sharpen research skills: giving good talks, experimental design, statistical analysis, and literature surveys.


This course combines lectures with paper presentations by the students, encouraging both fundamental knowledge acquisition as well as open-ended discussions. Each student will also carry out an individual research project OR an in-depth literature survey.

Learning Objectives

At the end of this course, you will have gained both knowledge/abilities related to human-robot interaction, as well as to research and presentation skills:


There are no official prerequisites but a knowledge of probability and multivariate calculus is expected.



You can expect me to start and end class on time, devise quizzes that adequately cover the material, and grade your quizzes and send you feedback on your presentations in a timely manner. In turn, I can expect you to come to class (pandemic update: Zoom) on time, be attentive and engaged in class, and refrain from using laptops (pandemic update: using laptops to do other tasks outside of attending lecture), cell phones and other electronic devices during class. Please take notes, and ask questions when something is not clear. I also expect you to spend an adequate amount of time on the readings each week (~3 hours), and spend ~60 hours on your final project.

Important Dates

Project Proposal Instructions

They should be 1 page (+ references).

If you are doing a research project:

If you are doing a literature survey:

Possible venues for projects


Find a tentative schedule below. This is subject to change.

# Date Topic Reading Notes
1 Jan 17 What is Algorithmic HRI? none slides
Part 1: How to optimize cost/reward
2 Jan 19 Trajectory Optimization 1 – Lecture
3 Jan 24 Trajectory Optimization 2 – Lecture presentations guide
example 1
example 2
4 Jan 26 Trajectory Optimization 3 – Lecture all trajopt notes
5 Jan 31 Traj Opt in robotics – Papers
  • "Elastic Bands: Connecting Path Planning and Control" (1993) link
  • ILQR link
6 Feb 2 Traj Opt in HRI – Papers
  • "Planning human-aware motions using a sampling-based costmap planner" (2011) link
  • “Spacetime constraints” (jumping Luxo lamp) (1988) link
7 Feb 7 Intro to MDPs, RL, POMDPs, Games – Lecture
Part 2: What cost/reward to optimize
8 Feb 9 Inverse Reinforcement Learning – Lecture Further reading:
  • "Maximum Margin Planning" (2006) link
  • "Maximum Entropy IRL" (2010) link
  • "Bayesian IRL" (2007) link
IRL notes
9 Feb 14 Learning rewards from human input – Lecture Further reading:
  • “Reward rational implicit choice” link
  • "Learning robot objectives from physical human interaction. link
  • "Preferences implicit in the state of the world. link
RRiC notes
10 Feb 16 Imitation Learning (aka skip the reward middleman) – Lecture Further reading:
  • "Learning Attractor Landscapes for Learning Motor Primitives" (2003) link
  • "Movement Primitives via Optimization" (2015) link
Imitation notes
11 Feb 21 How babies learn from human behavior – Papers
  • “Understanding the intentions of others” (1995) link

2 short papers:
  • "Rational Imitation in Preverbal Infants" (2002) link
  • and as background to explain it: "Infant Imitation After a 1-Week Delay" (1988) link 
12 Feb 23 Learning from feedback – Papers
  • "Deep Reinforcement Learning from Human Preferences" link
  • "Training language models to follow instructions with human feedback" link

Further reading:
  • Active "Preference-Based Learning of Reward Functions" link
  • "Learning Human Objectives by Evaluating Hypothetical Behavior" link
  • "Batch Active Preference-Based Learning for Reward Functions" link
13 Feb 28 Learning in HRI – Papers
  • "Trajectories and Keyframes for Kinesthetic Teaching" (2012) link
  • "Designing Robot Learners that Ask Good Questions" (2012) link

Further reading:
  • "Using Perspective Taking to Learn from Ambiguous Demonstrations" (2006) link
Part 3: Collaboration, assistance, and coordination
14 March 2 Designing intent expression - Papers
  • "Anticipation in Robot Motion" (2011) link
  • "Improving Robot Readability" (2011) link

Further reading:
  • "Communication of Intent in Assistive Free Flyers" (2014) link
15 March 7 Online intent inference and expression – Lecture Further reading "Planning Based Prediction for Pedestrians" (2009) link "Goal Inference as Invese Planning" (2007) link "Obsessed with Goals" (2007) link "Legibility and predictability of robot motion" (2013) link Intent notes
16 March 9 MPDs/POMDPs to avoid/assist with rational/imitative models – Lecture Further reading:
  • "CrossTraining" (2013) link
  • "Other play" link
Avoid/collab/assist notes
17 March 14 MDPs+avoid/collaborate – Papers
  • "Socially Compliant Navigation via IRL" link
  • "Predicting Human Reaching Motion" (2015) link
18 March 16 POMDPs+avoid/collaborate - Papers
  • "Shared Autonomy via Hidsight Optimization" (2015) link
  • "Intention-Aware Motion Planning" (2013) link
19 March 21 RL to collaborate - Papers
  • Human-aware RL link
  • Off belief learning link
Part 4: Experiment design
20 March 23 Experiment Design 1 – Lecture
21 March 28 Spring Break
22 March 30 Spring Break
23 April 4 Experiment Design 2 – Lecture
  • "Evaluating Fluency in Human-Robot Collaboration" (2013) link
Part 5: The frontier of AHRI
24 April 6 HRI as a Game – Lecture
25 April 11 HRI as a Game – Papers
  • Cooperative IRL (2016) link
  • Influence-aware planning (2016) link

Further reading:
26 April 13 Human models beyond imitation and noisy-rationality  – Lecture Further reading:
  • Human biases and reward inference link
  • The Boltzmann policy distribution link
27 April 18 Human models – Papers
  • How children come to understand false beliefs (2018) link
  • Where do you think you’re going (2018) link

Further reading:
  • Assisted Perception (2020) link
28 April 20 Human models – Papers 2
  • Modeling human-like gameplay (2022) link
  • Evolving negotiation agents (2022) link
29 April 25 Presentations 1
30 April 27 Presentations 2

For more readings, check out a few other class websites (this is by no means a comprehensive list):