9 accepted papers
A central challenge of visual control with model-based reinforcement learning (RL) is reliable long-horizon planning: Long rollouts with learned latent dynamics exhibit branching futures and multi-modal action-value distributions. In addition, compounding model error amplified by visual occlusions m…
We present a method that reduces, by an order of magnitude, the time and memory needed to train multi-task vision-language robotic diffusion policies. This improvement arises from a previously underexplored distinction between action diffusion and the image diffusion techniques that inspired it: In
We present a method that reduces, by an order of magnitude, the time and memory needed to train multi-task vision-language robotic diffusion policies. This improvement arises from a previously underexplored distinction between action diffusion and the image diffusion techniques that inspired it: In …
We introduce See&Trek, the first training-free prompting framework tailored to enhance the spatial understanding of Multimodal Large Language Models (MLLMs) under vision-only constraints. While prior efforts have incorporated modalities like depth or point clouds to improve spatial reasoning, purely…
There are two dominant approaches in modern robot grasp planning: dense prediction and sampling-based methods. Dense prediction calculates viable grasps across the robot’s view but is limited to predicting one grasp per voxel. Sampling-based methods, on the other hand, encode multi-modal grasp distr…
The autonomous driving community is highly interested in 3D occupancy prediction due to its outstanding geometric perception and object recognition capabilities. However, previous methods are limited to existing semantic conversion mechanisms for solving sparse ground truths problem, causing excessi…
We address the problem of (a) predicting the trajectory of an arm reaching motion, based on a few seconds of the motion’s onset, and (b) leveraging this predictor to facilitate shared-control manipulation tasks, by reducing the operator’s cognitive load through assistance in their anticipated direct…
Generic object detection methods have achieved preferable results, but it is still challenging to detect objects from complicated traffic scenes like extreme illumination and adverse weather. The existing methods are not robust enough to be extended to new complex traffic scenes. To address this iss…
Occluded person re-identification is a challenging task as human body parts could be occluded by some obstacles (e.g. trees, cars, and pedestrians) in certain scenes. Some existing pose-guided methods solve this problem by aligning body parts according to graph matching, but these graph-based method…