Differentiable simulators provide an avenue for closing the sim2real gap by enabling the use of efficient, gradient-based optimization algorithms to find the simulation parameters that best fit the observed sensor readings. Nonetheless, these …
Employing robots in the real world to perform a large variety of tasks remains a great challenge to current perception, planning and control algorithms. Various specialized representations, such as for mapping or localization, have been proposed which are typically used in fixed pipelines that fuse perception, planning and control.
We present a differentiable simulation architecture for articulated rigid-body dynamics that enables the augmentation of analytical models with neural networks at any point of the computation. Through gradient-based optimization, identification of …
A key ingredient to achieving intelligent behavior is physical understanding that equips robots with the ability to reason about the effects of their actions in a dynamic environment. Several methods have been proposed to learn dynamics models from …
Accurate simulations allow modern machine learning techniques to be applied to robotics problems, with sample-collection runtimes orders of magnitudes faster than the real world. Current reinforcement learning approaches require laborious manual …
Simulation-to-real transfer is an important strategy for making reinforcement learning practical with real robots. Successful sim-to-real transfer systems have difficulty producing policies which generalize across tasks, despite training for …
Studies of market structure and product market competition are important in many disciplines, such as economics, finance, accounting and management. Reliable data for such studies is easily available for public firms (e.g., 10-K filings), but no …