no code implementations • 11 Feb 2017 • Sandy H. Huang, David Held, Pieter Abbeel, Anca D. Dragan
We show that certain approximate-inference models lead to the robot generating example behaviors that better enable users to anticipate what it will do in novel situations.
no code implementations • 18 Oct 2018 • Sandy H. Huang, Kush Bhatia, Pieter Abbeel, Anca D. Dragan
In order to effectively interact with or supervise a robot, humans need to have an accurate mental model of its capabilities and how it acts.
Robotics
no code implementations • 21 Dec 2018 • Ravi Pandya, Sandy H. Huang, Dylan Hadfield-Menell, Anca D. Dragan
People frequently face challenging decision-making problems in which outcomes are uncertain or unknown.
no code implementations • 6 Nov 2019 • Sandy H. Huang, Isabella Huang, Ravi Pandya, Anca D. Dragan
Robots can learn preferences from human demonstrations, but their success depends on how informative these demonstrations are.
1 code implementation • 15 May 2020 • Abbas Abdolmaleki, Sandy H. Huang, Leonard Hasenclever, Michael Neunert, H. Francis Song, Martina Zambelli, Murilo F. Martins, Nicolas Heess, Raia Hadsell, Martin Riedmiller
Many real-world problems require trading off multiple competing objectives.
no code implementations • 15 Jun 2021 • Abbas Abdolmaleki, Sandy H. Huang, Giulia Vezzani, Bobak Shahriari, Jost Tobias Springenberg, Shruti Mishra, Dhruva TB, Arunkumar Byravan, Konstantinos Bousmalis, Andras Gyorgy, Csaba Szepesvari, Raia Hadsell, Nicolas Heess, Martin Riedmiller
Many advances that have improved the robustness and efficiency of deep reinforcement learning (RL) algorithms can, in one way or another, be understood as introducing additional objectives or constraints in the policy optimization step.
no code implementations • 26 Apr 2023 • Tuomas Haarnoja, Ben Moran, Guy Lever, Sandy H. Huang, Dhruva Tirumala, Jan Humplik, Markus Wulfmeier, Saran Tunyasuvunakool, Noah Y. Siegel, Roland Hafner, Michael Bloesch, Kristian Hartikainen, Arunkumar Byravan, Leonard Hasenclever, Yuval Tassa, Fereshteh Sadeghi, Nathan Batchelor, Federico Casarini, Stefano Saliceti, Charles Game, Neil Sreendra, Kushal Patel, Marlon Gwira, Andrea Huber, Nicole Hurley, Francesco Nori, Raia Hadsell, Nicolas Heess
We investigate whether Deep Reinforcement Learning (Deep RL) is able to synthesize sophisticated and safe movement skills for a low-cost, miniature humanoid robot that can be composed into complex behavioral strategies in dynamic environments.
1 code implementation • NeurIPS 2023 • Joe Watson, Sandy H. Huang, Nicolas Heess
Imitation learning methods seek to learn from an expert either through behavioral cloning (BC) of the policy or inverse reinforcement learning (IRL) of the reward.