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Joe Watson
Joe Watson
PhD Researcher, TU Darmstadt
Verified email at ias.informatik.tu-darmstadt.de - Homepage
Title
Cited by
Cited by
Year
Real-world, Real-time Robotic Grasping with Convolutional Neural Networks
J Watson, J Hughes, F Iida
Annual Conference Towards Autonomous Robotic Systems, 2017
422017
Differentiable Physics Models for Real-world Offline Model-based Reinforcement Learning
M Lutter, J Silberbauer, J Watson, J Peters
International Conference on Robotics and Automation, 2020
402020
Stochastic Optimal Control as Approximate Input Inference
J Watson, H Abdulsamad, J Peters
Conference on Robot Learning, 2020
312020
Control as Inference? Comparing Path Integral and Message Passing Methods for Optimal Control
J Watson
Reinforcement Learning Algorithms: Analysis and Applications, 2021
28*2021
Latent Derivative Bayesian Last Layer Networks
J Watson*, JA Lin*, P Klink, J Pajarinen, J Peters
International Conference on Artificial Intelligence and Statistics, 2021
272021
Benchmarking Structured Policies and Policy Optimization for Real-world Dexterous Object Manipulation
N Funk, C Schaff, R Madan, T Yoneda, JU De Jesus, J Watson, ...
IEEE Robotics and Automation Letters, 2021
242021
Real Robot Challenge: A Robotics Competition in the Cloud
S Bauer, M WŁthrich, F Widmaier, A Buchholz, S Stark, A Goyal, ...
NeurIPS 2021 Competitions and Demonstrations Track, 2022
14*2022
Active Inference or Control as Inference? A Unifying View
J Watson, A Imohiosen, J Peters
International Workshop on Active Inference, 2020
14*2020
A Differentiable Newton Euler Algorithm for Multi-body Model Learning
M Lutter, J Silberbauer, J Watson, J Peters
ICML Workshop on Inductive Biases, Invariances and Generalization in RL, 2020
122020
Neural Linear Models with Functional Gaussian Process Priors
J Watson*, JA Lin*, P Klink, J Peters
Advances in Approximate Bayesian Inference, 2021
102021
Inferring Smooth Control: Monte Carlo Posterior Policy Iteration with Gaussian Processes
J Watson, J Peters
Conference on Robot Learning, 2022
82022
Efficient Stochastic Optimal Control through Approximate Bayesian Input Inference
J Watson, H Abdulsamad, R Findeisen, J Peters
arXiv e-prints, arXiv: 2105.07693, 2021
8*2021
Advancing Trajectory Optimization with Approximate Inference: Exploration, Covariance Control and Adaptive Risk
J Watson, J Peters
American Control Conference, 2021
82021
Coherent Soft Imitation Learning
J Watson, SH Huang, N Heess
Advances in Neural Information Processing Systems, 2023
32023
A Differentiable Newton–Euler Algorithm for Real-World Robotics
M Lutter, J Silberbauer, J Watson, J Peters
Inductive Biases in Machine Learning for Robotics and Control, 2023
22023
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Articles 1–15