Learning fine-grained bimanual manipulation with low-cost hardware
Fine manipulation tasks, such as threading cable ties or slotting a battery, are notoriously
difficult for robots because they require precision, careful coordination of contact forces, and …
difficult for robots because they require precision, careful coordination of contact forces, and …
Eureka: Human-level reward design via coding large language models
Large Language Models (LLMs) have excelled as high-level semantic planners for
sequential decision-making tasks. However, harnessing them to learn complex low-level …
sequential decision-making tasks. However, harnessing them to learn complex low-level …
Multi-agent reinforcement learning is a sequence modeling problem
Large sequence models (SM) such as GPT series and BERT have displayed outstanding
performance and generalization capabilities in natural language process, vision and …
performance and generalization capabilities in natural language process, vision and …
ARCTIC: A dataset for dexterous bimanual hand-object manipulation
Humans intuitively understand that inanimate objects do not move by themselves, but that
state changes are typically caused by human manipulation (eg, the opening of a book). This …
state changes are typically caused by human manipulation (eg, the opening of a book). This …
Safety gymnasium: A unified safe reinforcement learning benchmark
Artificial intelligence (AI) systems possess significant potential to drive societal progress.
However, their deployment often faces obstacles due to substantial safety concerns. Safe …
However, their deployment often faces obstacles due to substantial safety concerns. Safe …
Toward general-purpose robots via foundation models: A survey and meta-analysis
Building general-purpose robots that operate seamlessly in any environment, with any
object, and utilizing various skills to complete diverse tasks has been a long-standing goal in …
object, and utilizing various skills to complete diverse tasks has been a long-standing goal in …
Meta-reward-net: Implicitly differentiable reward learning for preference-based reinforcement learning
Abstract Setting up a well-designed reward function has been challenging for many
reinforcement learning applications. Preference-based reinforcement learning (PbRL) …
reinforcement learning applications. Preference-based reinforcement learning (PbRL) …
Rlafford: End-to-end affordance learning for robotic manipulation
Learning to manipulate 3D objects in an interactive environment has been a challenging
problem in Reinforcement Learning (RL). In particular, it is hard to train a policy that can …
problem in Reinforcement Learning (RL). In particular, it is hard to train a policy that can …
ArtiGrasp: Physically plausible synthesis of bi-manual dexterous grasping and articulation
We present ArtiGrasp, a novel method to synthesize bimanual hand-object interactions that
include grasping and articulation. This task is challenging due to the diversity of the global …
include grasping and articulation. This task is challenging due to the diversity of the global …
Learning score-based grasping primitive for human-assisting dexterous grasping
The use of anthropomorphic robotic hands for assisting individuals in situations where
human hands may be unavailable or unsuitable has gained significant importance. In this …
human hands may be unavailable or unsuitable has gained significant importance. In this …