Acquiring a multi-task imitation policy in 3D manipulation poses challenges in terms of
scene understanding and action prediction. Current methods employ both 3D representation …

We study how visual representations pre-trained on diverse human video data can enable
data-efficient learning of downstream robotic manipulation tasks. Concretely, we pre-train a …

Modeling a generalized visuomotor policy has been a longstanding challenge for both
computer vision and robotics communities. Existing approaches often fail to efficiently …

Representation learning approaches for robotic manipulation have boomed in recent years.
Due to the scarcity of in-domain robot data, prevailing methodologies tend to leverage large …

3D perceptual representations are well suited for robot manipulation as they easily encode
occlusions and simplify spatial reasoning. Many manipulation tasks require high spatial …

Learning visual predictive models has great potential for real-world robot manipulations.
Visual predictive models serve as a model of real-world dynamics to comprehend the …

Autonomous robotic systems capable of learning novel manipulation tasks are poised to
transform industries from manufacturing to service automation. However, modern methods …

Learning general-purpose models from diverse datasets has achieved great success in
machine learning. In robotics, however, existing methods in multi-task learning are typically …

In this work, we focus on multi-step manipulation tasks that involve long-horizon planning
and considers progress reversal. Such tasks interlace high-level reasoning that consists of …

We propose a novel framework for learning high-level cognitive capabilities in robot
manipulation tasks, such as making a smiley face using building blocks. These tasks often …