CHOMP: Covariant Hamiltonian optimization for motion planning
- Matthew Zucker, Nathan D. Ratliff, S. Srinivasa
- Computer ScienceInt. J. Robotics Res.
- 1 August 2013
In this paper, we present CHOMP (covariant Hamiltonian optimization for motion planning), a method for trajectory optimization invariant to reparametrization. CHOMP uses functional gradient…
A policy-blending formalism for shared control
- A. Dragan, S. Srinivasa
- Computer ScienceInt. J. Robotics Res.
- 1 June 2013
This work proposes an intuitive formalism that captures assistance as policy blending, illustrates how some of the existing techniques for shared control instantiate it, and provides a principled analysis of its main components: prediction of user intent and its arbitration with the user input.
Legibility and predictability of robot motion
- A. Dragan, Kenton C. T. Lee, S. Srinivasa
- Computer ScienceIEEE/ACM International Conference on Human-Robot…
- 3 March 2013
The findings indicate that for robots to seamlessly collaborate with humans, they must change the way they plan their motion, and a formalism to mathematically define and distinguish predictability and legibility of motion is developed.
Cooperative Inverse Reinforcement Learning
- Dylan Hadfield-Menell, Stuart J. Russell, P. Abbeel, A. Dragan
- Computer ScienceNIPS
- 1 June 2016
It is shown that computing optimal joint policies in CIRL games can be reduced to solving a POMDP, it is proved that optimality in isolation is suboptimal in C IRL, and an approximate CirL algorithm is derived.
On the Utility of Learning about Humans for Human-AI Coordination
- Micah Carroll, Rohin Shah, A. Dragan
- Computer ScienceNeural Information Processing Systems
- 13 October 2019
A simple environment that requires challenging coordination, based on the popular game Overcooked, is introduced and a simple model is learned that mimics human play, and it is found that the gains come from having the agent adapt to the human's gameplay.
SQIL: Imitation Learning via Reinforcement Learning with Sparse Rewards
This work proposes a simple alternative that still uses RL, but does not require learning a reward function, and can be implemented with a handful of minor modifications to any standard Q-learning or off-policy actor-critic algorithm, called soft Q imitation learning (SQIL).
Planning for Autonomous Cars that Leverage Effects on Human Actions
- Dorsa Sadigh, S. Sastry, S. Seshia, A. Dragan
- Computer ScienceRobotics: Science and Systems
- 18 June 2016
The user study results suggest that the robot is indeed capable of eliciting desired changes in human state by planning using this dynamical system, in which the robot’s actions have immediate consequences on the state of the car, but also on human actions.
Toward seamless human-robot handovers
- K. Strabala, Min Kyung Lee, Vincenzo Micelli
- BusinessJournal of Human-Robot Interaction
- 27 February 2013
A coordination structure for human-robot handovers is proposed that considers the physical and social-cognitive aspects of the interaction separately and describes how people approach, reach out their hands, and transfer objects while simultaneously coordinating the what, when, and where of handovers.
Active Preference-Based Learning of Reward Functions
- Dorsa Sadigh, A. Dragan, S. Sastry, S. Seshia
- Computer ScienceRobotics: Science and Systems
- 12 July 2017
This work builds on work in label ranking and proposes to learn from preferences (or comparisons) instead: the person provides the system a relative preference between two trajectories, and takes an active learning approach, in which the system decides on what preference queries to make.
DART: Noise Injection for Robust Imitation Learning
- Michael Laskey, Jonathan Lee, Roy Fox, A. Dragan, Ken Goldberg
- Computer ScienceConference on Robot Learning
- 27 March 2017
A new algorithm is proposed, DART (Disturbances for Augmenting Robot Trajectories), that collects demonstrations with injected noise, and optimizes the noise level to approximate the error of the robot's trained policy during data collection.
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