8 papers
Warp RL: Reshaping Base Policy Distributions for Dynamics Adaptation
Ethan Hirschowitz, Fabio Ramos
Residual reinforcement learning adapts a pretrained robot policy by learning an additive correction to its actions. While effective when adaptation amounts to shifting the base pol…
Informing Acquisition Functions via Foundation Models for Molecular Discovery
Qi Chen, Fabio Ramos, Alán Aspuru-Guzik +1
Bayesian Optimization (BO) is a key methodology for accelerating molecular discovery by estimating the mapping from molecules to their properties while seeking the optimal candidat…
Harnessing Bounded-Support Evolution Strategies for Policy Refinement
Ethan Hirschowitz, Fabio Ramos
Improving competent robot policies with on-policy RL is often hampered by noisy, low-signal gradients. We revisit Evolution Strategies (ES) as a policy-gradient proxy and localize…
VLA-0: Building State-of-the-Art VLAs with Zero Modification
Ankit Goyal, Hugo Hadfield, Xuning Yang +2
Vision-Language-Action models (VLAs) hold immense promise for enabling generalist robot manipulation. However, the best way to build them remains an open question. Current approach…
Mass Conservation on Rails -- Rethinking Physics-Informed Learning of Ice Flow Vector Fields
Kim Bente, Roman Marchant, Fabio Ramos
To reliably project future sea level rise, ice sheet models require inputs that respect physics. Embedding physical principles like mass conservation into models that interpolate A…
Learning to Plan & Schedule with Reinforcement-Learned Bimanual Robot Skills
Weikang Wan, Fabio Ramos, Xuning Yang +1
Long-horizon contact-rich bimanual manipulation presents a significant challenge, requiring complex coordination involving a mixture of parallel execution and sequential collaborat…
Do What You Say: Steering Vision-Language-Action Models via Runtime Reasoning-Action Alignment Verification
Yilin Wu, Anqi Li, Tucker Hermans +3
Reasoning Vision Language Action (VLA) models improve robotic instruction-following by generating step-by-step textual plans before low-level actions, an approach inspired by Chain…
Dynamic Q-planning for Online UAV Path Planning in Unknown and Complex Environments
Lidia Gianne Souza da Rocha, Kenny Anderson Queiroz Caldas, Marco Henrique Terra +2
Unmanned Aerial Vehicles need an online path planning capability to move in high-risk missions in unknown and complex environments to complete them safely. However, many algorithms…