Search Results for author:
Found 26 papers, 10 papers with code
Physics-informed Neural Motion Planning on Constraint Manifolds
Constrained Motion Planning (CMP) aims to find a collision-free path between the given start and goal configurations on the kinematic constraint manifolds.
AnyPose: Anytime 3D Human Pose Forecasting via Neural Ordinary Differential Equations
Anytime 3D human pose forecasting is crucial to synchronous real-world human-machine interaction, where the term ``anytime" corresponds to predicting human pose at any real-valued time step.
Structural Concept Learning via Graph Attention for Multi-Level Rearrangement Planning
Robotic manipulation tasks, such as object rearrangement, play a crucial role in enabling robots to interact with complex and arbitrary environments.
SIMMF: Semantics-aware Interactive Multiagent Motion Forecasting for Autonomous Vehicle Driving
These encodings along with agents' local information, are passed through an encoder to obtain time-dependent latent variables for a motion policy predicting the future trajectories.
MANER: Multi-Agent Neural Rearrangement Planning of Objects in Cluttered Environments
Object rearrangement is a fundamental problem in robotics with various practical applications ranging from managing warehouses to cleaning and organizing home kitchens.
Progressive Learning for Physics-informed Neural Motion Planning
Neural motion planners (NMPs) demonstrate fast computational speed in finding path solutions but require a huge amount of expert trajectories for learning, thus adding a significant training computational load.
Merging Decision Transformers: Weight Averaging for Forming Multi-Task Policies
Recent work has shown the promise of creating generalist, transformer-based, models for language, vision, and sequential decision-making problems.
Co-learning Planning and Control Policies Constrained by Differentiable Logic Specifications
Synthesizing planning and control policies in robotics is a fundamental task, further complicated by factors such as complex logic specifications and high-dimensional robot dynamics.
Hierarchical Reinforcement Learning
reinforcement-learning
+2
Control Transformer: Robot Navigation in Unknown Environments through PRM-Guided Return-Conditioned Sequence Modeling
We show that Control Transformer can successfully navigate through mazes and transfer to unknown environments.
CoGrasp: 6-DoF Grasp Generation for Human-Robot Collaboration
Furthermore, our user study with 10 independent participants indicated our approach enables a safe, natural, and socially-aware human-robot objects' co-grasping experience compared to a standard robot grasping technique.
NTFields: Neural Time Fields for Physics-Informed Robot Motion Planning
We evaluate our method in various cluttered 3D environments, including the Gibson dataset, and demonstrate its ability to solve motion planning problems for 4-DOF and 6-DOF robot manipulators where the traditional grid-based Eikonal planners often face the curse of dimensionality.
Robot Active Neural Sensing and Planning in Unknown Cluttered Environments
Active sensing and planning in unknown, cluttered environments is an open challenge for robots intending to provide home service, search and rescue, narrow-passage inspection, and medical assistance.
Co-design of Embodied Neural Intelligence via Constrained Evolution
The agent is then randomly modified within the allowed ranges creating a new generation of several hundred agents.
Model-free Neural Lyapunov Control for Safe Robot Navigation
Model-free Deep Reinforcement Learning (DRL) controllers have demonstrated promising results on various challenging non-linear control tasks.
Motion Planning Transformers: A Motion Planning Framework for Mobile Robots
A popular technique to improve the efficiency of these planners is to restrict search space in the planning domain.
NeRP: Neural Rearrangement Planning for Unknown Objects
We propose NeRP (Neural Rearrangement Planning), a deep learning based approach for multi-step neural object rearrangement planning which works with never-before-seen objects, that is trained on simulation data, and generalizes to the real world.
MPC-MPNet: Model-Predictive Motion Planning Networks for Fast, Near-Optimal Planning under Kinodynamic Constraints
Kinodynamic Motion Planning (KMP) is to find a robot motion subject to concurrent kinematics and dynamics constraints.
Constrained Motion Planning Networks X
However, few solutions to constrained motion planning are available, and those that exist struggle with high computational time complexity in finding a path solution on the manifolds.
Neural Manipulation Planning on Constraint Manifolds
The presence of task constraints imposes a significant challenge to motion planning.
Motion Planning Networks: Bridging the Gap Between Learning-based and Classical Motion Planners
We validate MPNet against gold-standard and state-of-the-art planning methods in a variety of problems from 2D to 7D robot configuration spaces in challenging and cluttered environments, with results showing significant and consistently stronger performance metrics, and motivating neural planning in general as a modern strategy for solving motion planning problems efficiently.
Composing Task-Agnostic Policies with Deep Reinforcement Learning
The composition of elementary behaviors to solve challenging transfer learning problems is one of the key elements in building intelligent machines.
Neural Path Planning: Fixed Time, Near-Optimal Path Generation via Oracle Imitation
In this work, we introduce a novel way of producing fast and optimal motion plans for static environments by using a stepping neural network approach, called OracleNet.
Deeply Informed Neural Sampling for Robot Motion Planning
In this paper, we present a neural network-based adaptive sampler for motion planning called Deep Sampling-based Motion Planner (DeepSMP).
Adversarial Imitation via Variational Inverse Reinforcement Learning
We consider a problem of learning the reward and policy from expert examples under unknown dynamics.
Potentially Guided Bidirectionalized RRT* for Fast Optimal Path Planning in Cluttered Environments
The proposed algorithms greatly improve the convergence rate and have a more efficient memory utilization.
Motion Planning Networks
Fast and efficient motion planning algorithms are crucial for many state-of-the-art robotics applications such as self-driving cars.
