We observe that pre-trained large language models (LLMs) are capable of
...
Most successes in autonomous robotic assembly have been restricted to si...
Large language models (LLMs) have demonstrated exciting progress in acqu...
We present a framework that formulates visual question answering as modu...
For a robot to personalize physical assistance effectively, it must lear...
While interacting in the world is a multi-sensory experience, many robot...
Large language models excel at a wide range of complex tasks. However,
e...
Recent progress in large language models (LLMs) has demonstrated the abi...
Humans form mental images of 3D scenes to support counterfactual imagina...
Grounding language to the visual observations of a navigating agent can ...
Deformable objects manipulation can benefit from representations that
se...
Large language models (LLMs) trained on code completion have been shown ...
Recent works have shown how the reasoning capabilities of Large Language...
Autonomous fabric manipulation is a longstanding challenge in robotics, ...
We investigate pneumatic non-prehensile manipulation (i.e., blowing) as ...
Large foundation models can exhibit unique capabilities depending on the...
Though robot learning is often formulated in terms of discrete-time Mark...
Action representation is an important yet often overlooked aspect in
end...
Deformable object manipulation requires computationally efficient
repres...
We propose a new class of random feature methods for linearizing softmax...
Enabling robots to solve multiple manipulation tasks has a wide range of...
We find that across a wide range of robot policy learning scenarios, tre...
Does having visual priors (e.g. the ability to detect objects) facilitat...
We investigate the visual cross-embodiment imitation setting, in which a...
The ability to communicate intention enables decentralized multi-agent r...
Rearranging and manipulating deformable objects such as cables, fabrics,...
Robotic manipulation can be formulated as inducing a sequence of spatial...
This paper proposes a new action representation for learning to perform
...
Intelligent manipulation benefits from the capacity to flexibly control ...
Is it possible to learn policies for robotic assembly that can generaliz...
Transparent objects are a common part of everyday life, yet they possess...
We study the problem of learning physical object representations for rob...
We investigate whether a robot arm can learn to pick and throw arbitrary...
Skilled robotic manipulation benefits from complex synergies between
non...
We present Im2Pano3D, a convolutional neural network that generates a de...
This paper presents a robotic pick-and-place system that is capable of
g...
Access to large, diverse RGB-D datasets is critical for training RGB-D s...
This paper focuses on semantic scene completion, a task for producing a
...
Robot warehouse automation has attracted significant interest in recent
...
Matching local geometric features on real-world depth images is a challe...