Towards human-level bimanual dexterous manipulation with reinforcement learning
Achieving human-level dexterity is an important open problem in robotics. However, tasks of
dexterous hand manipulation even at the baby level are challenging to solve through …
dexterous hand manipulation even at the baby level are challenging to solve through …
Unidexgrasp++: Improving dexterous grasping policy learning via geometry-aware curriculum and iterative generalist-specialist learning
We propose a novel, object-agnostic method for learning a universal policy for dexterous
object grasping from realistic point cloud observations and proprioceptive information under …
object grasping from realistic point cloud observations and proprioceptive information under …
Gapartnet: Cross-category domain-generalizable object perception and manipulation via generalizable and actionable parts
For years, researchers have been devoted to generalizable object perception and
manipulation, where cross-category generalizability is highly desired yet underexplored. In …
manipulation, where cross-category generalizability is highly desired yet underexplored. In …
Where2explore: Few-shot affordance learning for unseen novel categories of articulated objects
Articulated object manipulation is a fundamental yet challenging task in robotics. Due to
significant geometric and semantic variations across object categories, previous …
significant geometric and semantic variations across object categories, previous …
Learning environment-aware affordance for 3d articulated object manipulation under occlusions
Perceiving and manipulating 3D articulated objects in diverse environments is essential for
home-assistant robots. Recent studies have shown that point-level affordance provides …
home-assistant robots. Recent studies have shown that point-level affordance provides …
Manipllm: Embodied multimodal large language model for object-centric robotic manipulation
Robot manipulation relies on accurately predicting contact points and end-effector directions
to ensure successful operation. However learning-based robot manipulation trained on a …
to ensure successful operation. However learning-based robot manipulation trained on a …
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) …
Partmanip: Learning cross-category generalizable part manipulation policy from point cloud observations
Learning a generalizable object manipulation policy is vital for an embodied agent to work in
complex real-world scenes. Parts, as the shared components in different object categories …
complex real-world scenes. Parts, as the shared components in different object categories …
Learning foresightful dense visual affordance for deformable object manipulation
Understanding and manipulating deformable objects (eg, ropes and fabrics) is an essential
yet challenging task with broad applications. Difficulties come from complex states and …
yet challenging task with broad applications. Difficulties come from complex states and …
Mate: Benchmarking multi-agent reinforcement learning in distributed target coverage control
Abstract We introduce the Multi-Agent Tracking Environment (MATE), a novel multi-agent
environment simulates the target coverage control problems in the real world. MATE hosts …
environment simulates the target coverage control problems in the real world. MATE hosts …