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About this paper
Sebastian Gomez-Gonzalez, Sergey Prokudin, Bernhard Scholkopf, Jan Peters
A deep conditional generative model, TVAE, provides accurate, low-latency, and uncertainty-quantified real-time trajectory predictions for high-speed robotics. It achieved an improved 98.9% hitting rate in robot table tennis, surpassing both physics-based and LSTM models in long-term accuracy.
Key insights
- Deep conditional generative models can effectively avoid the cumulative error propagation inherent in recursive data-driven methods, leading to more accurate long-term trajectory forecasts.
- Capturing trajectory uncertainty through a latent variable provides a robust probabilistic forecast, enabling more informed and safer decision-making in dynamic robotic tasks.
- A data-driven approach can implicitly account for complex, unobservable physical factors, leading to superior real-world prediction performance compared to physics-based models that struggle with incomplete information.
Summary by alphaXiv.
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