A review of recent trend in motion planning of industrial robots
Motion planning is an integral part of each robotic system. It is critical to develop an effective
motion in order to achieve a successful performance. The ability to generate a smooth …
motion in order to achieve a successful performance. The ability to generate a smooth …
Humanoid locomotion and manipulation: Current progress and challenges in control, planning, and learning
Humanoid robots have great potential to perform various human-level skills. These skills
involve locomotion, manipulation, and cognitive capabilities. Driven by advances in machine …
involve locomotion, manipulation, and cognitive capabilities. Driven by advances in machine …
Receding-horizon reinforcement learning approach for kinodynamic motion planning of autonomous vehicles
X Zhang, Y Jiang, Y Lu, X Xu - IEEE Transactions on Intelligent …, 2022 - ieeexplore.ieee.org
Kinodynamic motion planning is critical for autonomous vehicles with high maneuverability
in dynamic environments. However, obtaining near-optimal motion planning solutions with …
in dynamic environments. However, obtaining near-optimal motion planning solutions with …
A survey on the integration of machine learning with sampling-based motion planning
Sampling-based methods are widely adopted solutions for robot motion planning. The
methods are straightforward to implement, effective in practice for many robotic systems. It is …
methods are straightforward to implement, effective in practice for many robotic systems. It is …
Sampling-based exploration for reinforcement learning of dexterous manipulation
In this paper, we present a novel method for achieving dexterous manipulation of complex
objects, while simultaneously securing the object without the use of passive support …
objects, while simultaneously securing the object without the use of passive support …
Online and robust intermittent motion planning in dynamic and changing environments
In this article, we propose RRT-Q, an online and intermittent kinodynamic motion planning
framework for dynamic environments with unknown robot dynamics and unknown …
framework for dynamic environments with unknown robot dynamics and unknown …
Learning a Generalizable Trajectory Sampling Distribution for Model Predictive Control
We propose a sample-based model predictive control (MPC) method for collision-free
navigation that uses a normalizing flow as a sampling distribution, conditioned on the start …
navigation that uses a normalizing flow as a sampling distribution, conditioned on the start …
Fast kinodynamic planning on the constraint manifold with deep neural networks
Motion planning is a mature area of research in robotics with many well-established
methods based on optimization or sampling the state space, suitable for solving kinematic …
methods based on optimization or sampling the state space, suitable for solving kinematic …
Symmetry-Informed Reinforcement Learning and its Application to Low-Level Attitude Control of Quadrotors
Symmetry is ubiquitous in nature, physics, and mathematics. However, a classical symmetry-
agnostic reinforcement learning (RL) approach cannot guarantee to respect symmetry …
agnostic reinforcement learning (RL) approach cannot guarantee to respect symmetry …
R R: Rapid eXploration for Reinforcement learning via sampling-based reset distributions and imitation pre-training
We present a method for enabling Reinforcement Learning of motor control policies for
complex skills such as dexterous manipulation. We posit that a key difficulty for training such …
complex skills such as dexterous manipulation. We posit that a key difficulty for training such …