Heterogeneous systems manipulation, i.e., manipulating rigid objects via...
Anytime 3D human pose forecasting is crucial to synchronous real-world
h...
Robotic manipulation tasks, such as object rearrangement, play a crucial...
The robot exploration task has been widely studied with applications spa...
Autonomous vehicles require motion forecasting of their surrounding
mult...
Motion planning is integral to robotics applications such as autonomous
...
Motion planning (MP) is one of the core robotics problems requiring fast...
Non-monotone object rearrangement planning in confined spaces such as
ca...
Recent research efforts have yielded significant advancements in manipul...
Recent work has shown the promise of creating generalist, transformer-ba...
This paper presents a hierarchical reinforcement learning algorithm
cons...
Learning long-horizon tasks such as navigation has presented difficult
c...
Robot grasping is an actively studied area in robotics, mainly focusing ...
Neural Motion Planners (NMPs) have emerged as a promising tool for solvi...
Active sensing and planning in unknown, cluttered environments is an ope...
We introduce a novel co-design method for autonomous moving agents' shap...
Model-free Deep Reinforcement Learning (DRL) controllers have demonstrat...
Transformers have become the powerhouse of natural language processing a...
Robots will be expected to manipulate a wide variety of objects in compl...
Kinodynamic Motion Planning (KMP) is to find a robot motion subject to
c...
Constrained motion planning is a challenging field of research, aiming f...
Reliable real-time planning for robots is essential in today's rapidly
e...
The presence of task constraints imposes a significant challenge to moti...
This paper describes Motion Planning Networks (MPNet), a computationally...
Composition of elementary skills into complex behaviors to solve challen...
Fast and efficient path generation is critical for robots operating in
c...
Sampling-based Motion Planners (SMPs) have become increasingly popular a...
We consider a problem of learning a reward and policy from expert exampl...
Rapidly-exploring Random Tree star (RRT*) has recently gained immense
po...
Fast and efficient motion planning algorithms are crucial for many
state...