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Peter Werner

dblp:09/4986 · DBLP profile ↗
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5ranked-venue papers
2as first author
4since 2021 · last 2024
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 5 · 2 first-author · 4 since 2021Systems, architecture and hardware · 3 · 2 first-author · 2 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
3 papers
Motion planning and robot control · 76% Reinforcement learning · 24%
Theoretical computer science
1 paper
Computational geometry · 100%

Topics — the 9 heaviest of 9, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Motion planning and robot control
motion planning
1.522024
Approximating Robot Configuration Spaces with few Convex Sets using Clique Covers of Visibility Graphs · ICRA 2024
Certifying Bimanual RRT Motion Plans in a Second · ICRA 2024
Robotics › Motion planning and robot control › motion planning › geometric motion planning
configuration space decomposition
0.812024
Approximating Robot Configuration Spaces with few Convex Sets using Clique Covers of Visibility Graphs · ICRA 2024
Robotics › Motion planning and robot control › motion planning › sampling-based motion planning
RRT
0.812024
Certifying Bimanual RRT Motion Plans in a Second · ICRA 2024
Robotics › Motion planning and robot control › motion planning
sampling-based motion planning
0.812024
Certifying Bimanual RRT Motion Plans in a Second · ICRA 2024
Computational geometry › polygon decomposition
convex decomposition
0.812024
Approximating Robot Configuration Spaces with few Convex Sets using Clique Covers of Visibility Graphs · ICRA 2024
Machine learning › Reinforcement learning
continuous control
0.712023
Solving Continuous Control via Q-learning · ICLR 2023
Machine learning › Reinforcement learning › value-based reinforcement learning
q-learning
0.712023
Solving Continuous Control via Q-learning · ICLR 2023
Robotics › Motion planning and robot control › collision avoidance
collision-free trajectory
0.212024
Approximating Robot Configuration Spaces with few Convex Sets using Clique Covers of Visibility Graphs · ICRA 2024
Robotics › Motion planning and robot control
trajectory optimization
0.212024
Certifying Bimanual RRT Motion Plans in a Second · ICRA 2024

Methods — techniques the papers use, named apart from their topics

visibility graph · 1.5convex optimization · 1.5clique cover · 1.5sums-of-squares optimization · 0.8algebraic reparametrization · 0.8q-learning · 0.7actor-critic · 0.7
YearPublicationVenuePosition
2024 Certifying Bimanual RRT Motion Plans in a Second
abstract
We present an efficient method for certifying non-collision for piecewise-polynomial motion plans in algebraic reparametrizations of configuration space. Such motion plans include those generated by popular randomized methods including RRTs and PRMs, as well as those generated by many methods in trajectory optimization. Based on Sums-of-Squares optimization, our method provides exact, rigorous certificates of non-collision; it can never falsely claim that a motion plan containing collisions is collision-free. We demonstrate that our formulation is practical for real world deployment, certifying the safety of a twelve degree of freedom motion plan in just over a second. Moreover, the method is capable of discriminating the safety or lack thereof of two motion plans which differ by only millimeters.
Alexandre Amice, Peter Werner, Russ Tedrake
ICRA2
2024 Approximating Robot Configuration Spaces with few Convex Sets using Clique Covers of Visibility Graphs
abstract
Many computations in robotics can be dramatically accelerated if the robot configuration space is described as a collection of simple sets. For example, recently developed motion planners rely on a convex decomposition of the free space to design collision-free trajectories using fast convex optimization. In this work, we present an efficient method for approximately covering complex configuration spaces with a small number of polytopes. The approach constructs a visibility graph using sampling and generates a clique cover of this graph to find clusters of samples that have mutual line of sight. These clusters are then inflated into large, full-dimensional, polytopes. We evaluate our method on a variety of robotic systems and show that it consistently covers larger portions of free configuration space, with fewer polytopes, and in a fraction of the time compared to previous methods.
Peter Werner, Alexandre Amice, Tobia Marcucci, Daniela Rus, Russ Tedrake
ICRA1
2023 Solving Continuous Control via Q-learning
Tim Seyde, Peter Werner, Wilko Schwarting, Igor Gilitschenski, Martin A. Riedmiller, Daniela Rus, Markus Wulfmeier
ICLR2
2022 Finding and Optimizing Certified, Collision-Free Regions in Configuration Space for Robot Manipulators
Alexandre Amice, Hongkai Dai, Peter Werner, Annan Zhang, Russ Tedrake
WAFR3
2020 Vision-Based Proprioceptive Sensing: Tip Position Estimation for a Soft Inflatable Bellow Actuator
abstract
This paper presents a vision-based sensing approach for a soft linear actuator, which is equipped with an internal camera. The proposed vision-based sensing pipeline predicts the three-dimensional tip position of the actuator. To train and evaluate the algorithm, predictions are compared to ground truth data from an external motion capture system. An off-the-shelf distance sensor is integrated in a second actuator of the same type, providing only the vertical component of the tip position and used as a baseline for comparison. The camera-based sensing pipeline runs at 40 Hz in real-time on a standard laptop and is additionally used for closed loop elongation control of the actuator. It is shown that the approach can achieve comparable accuracy to the distance sensor for measuring the linear expansion of the actuator, but additionally provide the full three-dimensional tip position.
Peter Werner, Matthias Hofer 0003, Carmelo Sferrazza, Raffaello D'Andrea
IROS1