6 papers
Rho: A Foundation for Efficiently Adaptable VLA Models
Rho Team, Simran Bagaria, Daphne Chen +11
General-purpose physical AI models must combine broad visual and linguistic capabilities with precise control across robot embodiments and efficient adaptation to downstream tasks.…
FlowDAgger: Human-in-the-Loop Adaptation of Generative Robot Policies in Latent Space
Michael Murray, Daphne Chen, Simran Bagaria +7
Pretrained generative robot policies based on flow matching and diffusion have achieved impressive results across a wide range of manipulation tasks. Yet real-world deployments rou…
Emergent Dexterity via Diverse Resets and Large-Scale Reinforcement Learning
Patrick Yin, Tyler Westenbroek, Zhengyu Zhang +9
Reinforcement learning in massively parallel physics simulations has driven major progress in sim-to-real robot learning. However, current approaches remain brittle and task-specif…
Benchmarking Affordance Generalization with BusyBox
Dean Fortier, Timothy Adamson, Tess Hellebrekers +5
Vision-Language-Action (VLA) models have been attracting the attention of researchers and practitioners thanks to their promise of generalization. Although single-task policies sti…
LoLA: Long Horizon Latent Action Learning for General Robot Manipulation
Xiaofan Wang, Xingyu Gao, Jianlong Fu +5
The capability of performing long-horizon, language-guided robotic manipulation tasks critically relies on leveraging historical information and generating coherent action sequence…
Rapid Co-design of Task-Specialized Whegged Robots for Ad-Hoc Needs
Varun Madabushi, Katie M. Popek, Craig Knuth +2
In this work, we investigate the use of co-design methods to iterate upon robot designs in the field, performing time sensitive, ad-hoc tasks. Our method optimizes the morphology a…