25 citations · 51 across the 47 of their papers we have counts for
11 papers · 1 filter
Memory Without Curation: Learning Multi-Stream Latent Memory for Robot Policies
Yilin Wu, Rutav Shah, Shuaiyi Huang +5
It is widely appreciated that robotic policies require long-term memory to perform complex tasks. However, directly conditioning policies on all past observations leaves policy lea…
Modeling Latent Disturbances for Robust Decision-Making in World Models
Junwon Seo, Andrea Bajcsy
In this paper, we study robust decision-making in the latent space of world models (WMs). Robust optimization is a mathematical framework where, given explicitly specified dynamics…
Rethinking Safety for Generalist Robots
Rohan Sinha, Anushri Dixit, Ran Tian +2
Generalist robots promise to transform our society: the same system that prepares a meal or folds laundry might also repair a car, inspect infrastructure, or care for a loved one.…
ELASTIC: Efficiently Learning to Adaptively Scale Test-Time Compute for Generative Control Policies
Andrew Zou Li, Gokul Swamy, Yonatan Bisk +1
Generative control policies (GCPs), such as diffusion policies and flow-based vision-language-action models, enable test-time scaling in robot control. Test-time compute can be all…
WEAVER, Better, Faster, Longer: An Effective World Model for Robotic Manipulation
Arnav Kumar Jain, Yilin Wu, Jesse Farebrother +2
The potential impacts of world models (WMs, i.e., learned simulators) on robotics are far-reaching -- policy evaluation, policy improvement, and test-time planning -- all with limi…
Inference-time Policy Steering via Vision and Touch
Yilin Wu, Zilin Si, Zeynep Temel +2
Inference-time steering adapts pre-trained generative robot policies during deployment by verifying candidate actions before execution. While prior methods typically perform this v…
Learning What to Say to Your VLA: Mostly Harmless Vision Language Action Model Steering
Hyun Joe Jeong, Gokul Swamy, Andrea Bajcsy
Vision-Language-Action (VLA) models provide a natural language interface to robot control, but the mapping from language to behavior is often brittle and unintuitive: semantically…
Position: Good Embodied Reward Models Need Bad Behavior Data
Ran Tian, Yilin Wu, Andrea Bajcsy
This position paper argues that to obtain reliable embodied reward models, the community must invest in ``bad'' robot data: failed, suboptimal, error-prone, and even hazardous beha…
StressDream: Steering Video World Models for Robust Policy Evaluation and Improvement
Junwon Seo, Sushant Veer, Ran Tian +6
Video world models (WMs) have shown promise for policy evaluation and improvement by imagining realistic future observations conditioned on ego-robot actions. While WMs can model d…
Coordinated Diffusion: Generating Multi-Agent Behavior Without Multi-Agent Demonstrations
Lasse Peters, Laura Ferranti, Andrea Bajcsy +1
Imitation learning powered by generative models has proven effective for modeling complex single-agent behaviors. However, teaching multi-agent systems, like multiple arms or vehic…
When to Act, Ask, or Learn: Uncertainty-Aware Policy Steering
Jessie Yuan, Yilin Wu, Andrea Bajcsy
Policy steering is an emerging way to adapt robot behaviors at deployment-time: a learned verifier analyzes low-level action samples proposed by a pre-trained policy (e.g., diffusi…