16 citations · 43 across the 10 of their papers we have counts for
12 papers
Geometric Fabrics: Generalizing Classical Mechanics to Capture the Physics of Behavior
Karl Van Wyk, Mandy Xie, Anqi Li +9
Classical mechanical systems are central to controller design in energy shaping methods of geometric control. However, their expressivity is limited by position-only metrics and th…
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…
Towards Coordinated Robot Motions: End-to-End Learning of Motion Policies on Transform Trees
M. Asif Rana, Anqi Li, Dieter Fox +3
Generating robot motion that fulfills multiple tasks simultaneously is challenging due to the geometric constraints imposed by the robot. In this paper, we propose to solve multi-t…
Optimization Fabrics for Behavioral Design
Nathan D. Ratliff, Karl Van Wyk, Mandy Xie +2
A common approach to the provably stable design of reactive behavior, exemplified by operational space control, is to reduce the problem to the design of virtual classical mechanic…
Geometric Fabrics for the Acceleration-based Design of Robotic Motion
Mandy Xie, Karl Van Wyk, Anqi Li +5
This paper describes the pragmatic design and construction of geometric fabrics for shaping a robot's task-independent nominal behavior, capturing behavioral components such as obs…
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…
Euclideanizing Flows: Diffeomorphic Reduction for Learning Stable Dynamical Systems
Muhammad Asif Rana, Anqi Li, Dieter Fox +3
Robotic tasks often require motions with complex geometric structures. We present an approach to learn such motions from a limited number of human demonstrations by exploiting the…
Taking Recoveries to Task: Recovery-Driven Development for Recipe-based Robot Tasks
Siddhartha Banerjee, Angel Daruna, David Kent +9
Robot task execution when situated in real-world environments is fragile. As such, robot architectures must rely on robust error recovery, adding non-trivial complexity to highly-c…
Benchmark for Skill Learning from Demonstration: Impact of User Experience, Task Complexity, and Start Configuration on Performance
M. Asif Rana, Daphne Chen, S. Reza Ahmadzadeh +3
In this work, we contribute a large-scale study benchmarking the performance of multiple motion-based learning from demonstration approaches. Given the number and diversity of exis…
Skill Acquisition via Automated Multi-Coordinate Cost Balancing
Harish Ravichandar, S. Reza Ahmadzadeh, M. Asif Rana +1
We propose a learning framework, named Multi-Coordinate Cost Balancing (MCCB), to address the problem of acquiring point-to-point movement skills from demonstrations. MCCB encodes…
Learning Generalizable Robot Skills from Demonstrations in Cluttered Environments
Muhammad Asif Rana, Mustafa Mukadam, Seyed Reza Ahmadzadeh +2
Learning from Demonstration (LfD) is a popular approach to endowing robots with skills without having to program them by hand. Typically, LfD relies on human demonstrations in clut…