On the opportunities and risks of foundation models
AI is undergoing a paradigm shift with the rise of models (eg, BERT, DALL-E, GPT-3) that are
trained on broad data at scale and are adaptable to a wide range of downstream tasks. We …
trained on broad data at scale and are adaptable to a wide range of downstream tasks. We …
No, to the right: Online language corrections for robotic manipulation via shared autonomy
Systems for language-guided human-robot interaction must satisfy two key desiderata for
broad adoption: adaptivity and learning efficiency. Unfortunately, existing instruction …
broad adoption: adaptivity and learning efficiency. Unfortunately, existing instruction …
Vat-mart: Learning visual action trajectory proposals for manipulating 3d articulated objects
Perceiving and manipulating 3D articulated objects (eg, cabinets, doors) in human
environments is an important yet challenging task for future home-assistant robots. The …
environments is an important yet challenging task for future home-assistant robots. The …
[HTML][HTML] Learning latent actions to control assistive robots
Assistive robot arms enable people with disabilities to conduct everyday tasks on their own.
These arms are dexterous and high-dimensional; however, the interfaces people must use …
These arms are dexterous and high-dimensional; however, the interfaces people must use …
[HTML][HTML] Intention-reflected predictive display for operability improvement of time-delayed teleoperation system
Y Zhu, K Fusano, T Aoyama, Y Hasegawa - ROBOMECH Journal, 2023 - Springer
Robotic teleoperation is highly valued for its ability to remotely execute tasks that demand
sophisticated human decision-making or that are intended to be carried out by human …
sophisticated human decision-making or that are intended to be carried out by human …
Efficient Planning with Latent Diffusion
W Li - arXiv preprint arXiv:2310.00311, 2023 - arxiv.org
Temporal abstraction and efficient planning pose significant challenges in offline
reinforcement learning, mainly when dealing with domains that involve temporally extended …
reinforcement learning, mainly when dealing with domains that involve temporally extended …
Oscar: Data-driven operational space control for adaptive and robust robot manipulation
J Wong, V Makoviychuk… - … on Robotics and …, 2022 - ieeexplore.ieee.org
Learning performant robot manipulation policies can be challenging due to high-
dimensional continuous actions and complex physics-based dynamics. This can be …
dimensional continuous actions and complex physics-based dynamics. This can be …
Just round: Quantized observation spaces enable memory efficient learning of dynamic locomotion
L Grossman, B Plancher - 2023 IEEE International Conference …, 2023 - ieeexplore.ieee.org
Deep reinforcement learning (DRL) is one of the most powerful tools for synthesizing
complex robotic behaviors. But training DRL models is incredibly compute and memory …
complex robotic behaviors. But training DRL models is incredibly compute and memory …
Semi-Autonomous Teleoperation via Learning Non-Prehensile Manipulation Skills
In this paper, we present a semi-autonomous teleoperation framework for a pick-and-place
task using an RGB-D sensor. In particular, we assume that the target object is located in a …
task using an RGB-D sensor. In particular, we assume that the target object is located in a …
Towards learning generalizable driving policies from restricted latent representations
Training intelligent agents that can drive autonomously in various urban and highway
scenarios has been a hot topic in the robotics society within the last decades. However, the …
scenarios has been a hot topic in the robotics society within the last decades. However, the …