1 citations · 1 across the 8 of their papers we have counts for
5 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…
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…
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…
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…