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Found 8 papers, 2 papers with code
Coherent Soft Imitation Learning
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.
Learning Agile Soccer Skills for a Bipedal Robot with Deep Reinforcement Learning
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.
On Multi-objective Policy Optimization as a Tool for Reinforcement Learning: Case Studies in Offline RL and Finetuning
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.
A Distributional View on Multi-Objective Policy Optimization
Many real-world problems require trading off multiple competing objectives.
Nonverbal Robot Feedback for Human Teachers
Robots can learn preferences from human demonstrations, but their success depends on how informative these demonstrations are.
Human-AI Learning Performance in Multi-Armed Bandits
People frequently face challenging decision-making problems in which outcomes are uncertain or unknown.
Establishing Appropriate Trust via Critical States
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
Enabling Robots to Communicate their Objectives
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.
