1 citations · 1 across the 8 of their papers we have counts for
10 papers
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…
PanoNav: Mapless Zero-Shot Object Navigation with Panoramic Scene Parsing and Dynamic Memory
Qunchao Jin, Yilin Wu, Changhao Chen
Zero-shot object navigation (ZSON) in unseen environments remains a challenging problem for household robots, requiring strong perceptual understanding and decision-making capabili…
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…
From Foresight to Forethought: VLM-In-the-Loop Policy Steering via Latent Alignment
Yilin Wu, Ran Tian, Gokul Swamy +1
While generative robot policies have demonstrated significant potential in learning complex, multimodal behaviors from demonstrations, they still exhibit diverse failures at deploy…
Maximizing Alignment with Minimal Feedback: Efficiently Learning Rewards for Visuomotor Robot Policy Alignment
Ran Tian, Yilin Wu, Chenfeng Xu +3
Visuomotor robot policies, increasingly pre-trained on large-scale datasets, promise significant advancements across robotics domains. However, aligning these policies with end-use…
HACMan++: Spatially-Grounded Motion Primitives for Manipulation
Bowen Jiang, Yilin Wu, Wenxuan Zhou +2
Although end-to-end robot learning has shown some success for robot manipulation, the learned policies are often not sufficiently robust to variations in object pose or geometry. T…