10 citations · 53 across the 16 of their papers we have counts for
15 papers · 1 filter
Isaac Lab: A GPU-Accelerated Simulation Framework for Multi-Modal Robot Learning
NVIDIA, :, Mayank Mittal +104
We present Isaac Lab, the natural successor to Isaac Gym, which extends the paradigm of GPU-native robotics simulation into the era of large-scale multi-modal learning. Isaac Lab c…
First Plan Then Evaluate: Multi-Target Planning with Post-Planning Success Evaluation Improves Learning-Based Grasping Pipelines
Martin Matak, Mohanraj Devendran Shanthi, Karl Van Wyk +1
Autonomous multi-finger grasping is a fundamental capability in robotic manipulation. Optimization-based approaches show strong performance, but tend to be sensitive to initializat…
23 DoF Grasping Policies from a Raw Point Cloud
Martin Matak, Karl Van Wyk, Tucker Hermans
Coordinating the motion of robots with high degrees of freedom (DoF) to grasp objects gives rise to many challenges. In this paper, we propose a novel imitation learning approach t…
DextrAH-G: Pixels-to-Action Dexterous Arm-Hand Grasping with Geometric Fabrics
Tyler Ga Wei Lum, Martin Matak, Viktor Makoviychuk +5
A pivotal challenge in robotics is achieving fast, safe, and robust dexterous grasping across a diverse range of objects, an important goal within industrial applications. However,…
cuRobo: Parallelized Collision-Free Minimum-Jerk Robot Motion Generation
Balakumar Sundaralingam, Siva Kumar Sastry Hari, Adam Fishman +9
This paper explores the problem of collision-free motion generation for manipulators by formulating it as a global motion optimization problem. We develop a parallel optimization t…
AnyTeleop: A General Vision-Based Dexterous Robot Arm-Hand Teleoperation System
Yuzhe Qin, Wei Yang, Binghao Huang +5
Vision-based teleoperation offers the possibility to endow robots with human-level intelligence to physically interact with the environment, while only requiring low-cost camera se…
Learning Robust Real-World Dexterous Grasping Policies via Implicit Shape Augmentation
Zoey Qiuyu Chen, Karl Van Wyk, Yu-Wei Chao +4
Dexterous robotic hands have the capability to interact with a wide variety of household objects to perform tasks like grasping. However, learning robust real world grasping polici…
DeXtreme: Transfer of Agile In-hand Manipulation from Simulation to Reality
Ankur Handa, Arthur Allshire, Viktor Makoviychuk +11
Recent work has demonstrated the ability of deep reinforcement learning (RL) algorithms to learn complex robotic behaviours in simulation, including in the domain of multi-fingered…
Imitation Learning via Simultaneous Optimization of Policies and Auxiliary Trajectories
Mandy Xie, Anqi Li, Karl Van Wyk +3
Imitation learning (IL) is a frequently used approach for data-efficient policy learning. Many IL methods, such as Dataset Aggregation (DAgger), combat challenges like distribution…
RMP2: A Structured Composable Policy Class for Robot Learning
Anqi Li, Ching-An Cheng, M. Asif Rana +4
We consider the problem of learning motion policies for acceleration-based robotics systems with a structured policy class specified by RMPflow. RMPflow is a multi-task control fra…
Generalized Nonlinear and Finsler Geometry for Robotics
Nathan D. Ratliff, Karl Van Wyk, Mandy Xie +2
Robotics research has found numerous important applications of Riemannian geometry. Despite that, the concept remain challenging to many roboticists because the background material…
Optimization Fabrics
Nathan D. Ratliff, Karl Van Wyk, Mandy Xie +2
This paper presents a theory of optimization fabrics, second-order differential equations that encode nominal behaviors on a space and can be used to define the behavior of a smoot…
Interpreting and Predicting Tactile Signals via a Physics-Based and Data-Driven Framework
Yashraj S. Narang, Karl Van Wyk, Arsalan Mousavian +1
High-density afferents in the human hand have long been regarded as essential for human grasping and manipulation abilities. In contrast, robotic tactile sensors are typically used…
In-Hand Object Pose Tracking via Contact Feedback and GPU-Accelerated Robotic Simulation
Jacky Liang, Ankur Handa, Karl Van Wyk +3
Tracking the pose of an object while it is being held and manipulated by a robot hand is difficult for vision-based methods due to significant occlusions. Prior works have explored…
Efficiently Improving and Quantifying Robot Accuracy In Situ
Karl Van Wyk, Joe Falco, Geraldine Cheok
The advancement of simulation-assisted robot programming, automation of high-tolerance assembly operations, and improvement of real-world performance engender a need for positional…