Search Results for author:
Found 11 papers, 1 papers with code
Finetuning from Offline Reinforcement Learning: Challenges, Trade-offs and Practical Solutions
While offline RL algorithms can in principle be used for finetuning, in practice, their online performance improves slowly.
Queer In AI: A Case Study in Community-Led Participatory AI
We present Queer in AI as a case study for community-led participatory design in AI.
Subverting machines, fluctuating identities: Re-learning human categorization
Most machine learning systems that interact with humans construct some notion of a person's "identity," yet the default paradigm in AI research envisions identity with essential attributes that are discrete and static.
A Generalist Agent
Inspired by progress in large-scale language modeling, we apply a similar approach towards building a single generalist agent beyond the realm of text outputs.
Ranked #1 on
Skill Generalization
on RGB-Stacking
Few-Shot Keypoint Detection as Task Adaptation via Latent Embeddings
Dense object tracking, the ability to localize specific object points with pixel-level accuracy, is an important computer vision task with numerous downstream applications in robotics.
Fairness for Unobserved Characteristics: Insights from Technological Impacts on Queer Communities
Advances in algorithmic fairness have largely omitted sexual orientation and gender identity.
Learning Dexterous Manipulation from Suboptimal Experts
Although in many cases the learning process could be guided by demonstrations or other suboptimal experts, current RL algorithms for continuous action spaces often fail to effectively utilize combinations of highly off-policy expert data and on-policy exploration data.
Local Search for Policy Iteration in Continuous Control
We demonstrate that additional computation spent on model-based policy improvement during learning can improve data efficiency, and confirm that model-based policy improvement during action selection can also be beneficial.
Modelling Generalized Forces with Reinforcement Learning for Sim-to-Real Transfer
Learning robotic control policies in the real world gives rise to challenges in data efficiency, safety, and controlling the initial condition of the system.
Self-Supervised Sim-to-Real Adaptation for Visual Robotic Manipulation
In this work, we learn a latent state representation implicitly with deep reinforcement learning in simulation, and then adapt it to the real domain using unlabeled real robot data.
Robust Reinforcement Learning for Continuous Control with Model Misspecification
We provide a framework for incorporating robustness -- to perturbations in the transition dynamics which we refer to as model misspecification -- into continuous control Reinforcement Learning (RL) algorithms.
