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Found 10 papers, 7 papers with code
ManipLLM: Embodied Multimodal Large Language Model for Object-Centric Robotic Manipulation
By fine-tuning the injected adapters, we preserve the inherent common sense and reasoning ability of the MLLMs while equipping them with the ability for manipulation.
Grasp Multiple Objects with One Hand
The intricate kinematics of the human hand enable simultaneous grasping and manipulation of multiple objects, essential for tasks such as object transfer and in-hand manipulation.
Safety-Gymnasium: A Unified Safe Reinforcement Learning Benchmark
By introducing this benchmark, we aim to facilitate the evaluation and comparison of safety performance, thus fostering the development of reinforcement learning for safer, more reliable, and responsible real-world applications.
OmniSafe: An Infrastructure for Accelerating Safe Reinforcement Learning Research
AI systems empowered by reinforcement learning (RL) algorithms harbor the immense potential to catalyze societal advancement, yet their deployment is often impeded by significant safety concerns.
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.
GraspNeRF: Multiview-based 6-DoF Grasp Detection for Transparent and Specular Objects Using Generalizable NeRF
In this work, we tackle 6-DoF grasp detection for transparent and specular objects, which is an important yet challenging problem in vision-based robotic systems, due to the failure of depth cameras in sensing their geometry.
GenDexGrasp: Generalizable Dexterous Grasping
By leveraging the contact map as a hand-agnostic intermediate representation, GenDexGrasp efficiently generates diverse and plausible grasping poses with a high success rate and can transfer among diverse multi-fingered robotic hands.
End-to-End Affordance Learning for Robotic Manipulation
Such contact prediction process then leads to an end-to-end affordance learning framework that can generalize over different types of manipulation tasks.
Towards Human-Level Bimanual Dexterous Manipulation with Reinforcement Learning
In this study, we propose the Bimanual Dexterous Hands Benchmark (Bi-DexHands), a simulator that involves two dexterous hands with tens of bimanual manipulation tasks and thousands of target objects.
GraspARL: Dynamic Grasping via Adversarial Reinforcement Learning
we formulate the dynamic grasping problem as a 'move-and-grasp' game, where the robot is to pick up the object on the mover and the adversarial mover is to find a path to escape it.
