[go: up one dir, main page]

Heiden et al., 2019 - Google Patents

Interactive differentiable simulation

Heiden et al., 2019

View PDF
Document ID
14590294895991084491
Author
Heiden E
Millard D
Zhang H
Sukhatme G
Publication year
Publication venue
arXiv preprint arXiv:1905.10706

External Links

Snippet

Intelligent agents need a physical understanding of the world to predict the impact of their actions in the future. While learning-based models of the environment dynamics have contributed to significant improvements in sample efficiency compared to model-free …
Continue reading at arxiv.org (PDF) (other versions)

Classifications

    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00Program-control systems
    • G05B2219/30Nc systems
    • G05B2219/39Robotics, robotics to robotics hand
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00Subject matter not provided for in other groups of this subclass
    • G06N99/005Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00Program-control systems
    • G05B2219/30Nc systems
    • G05B2219/40Robotics, robotics mapping to robotics vision
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00Programme-control systems
    • G05B19/02Programme-control systems electric
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models

Similar Documents

Publication Publication Date Title
Heiden et al. Interactive differentiable simulation
Dean et al. Robust guarantees for perception-based control
Chen et al. Hardware conditioned policies for multi-robot transfer learning
Lutter et al. Deep lagrangian networks: Using physics as model prior for deep learning
Sutanto et al. Encoding physical constraints in differentiable newton-euler algorithm
Rana et al. Towards robust skill generalization: Unifying learning from demonstration and motion planning
Xu et al. Compare contact model-based control and contact model-free learning: A survey of robotic peg-in-hole assembly strategies
Polydoros et al. Survey of model-based reinforcement learning: Applications on robotics
Patil et al. Scaling up gaussian belief space planning through covariance-free trajectory optimization and automatic differentiation
Gupta et al. Structured mechanical models for robot learning and control
Englert et al. Combined Optimization and Reinforcement Learning for Manipulation Skills.
Agia et al. Stap: Sequencing task-agnostic policies
Wang et al. Collision-free trajectory planning for a 6-DoF free-floating space robot via hierarchical decoupling optimization
Campbell et al. Bayesian interaction primitives: A slam approach to human-robot interaction
Li et al. Kinematic control of redundant robot arms using neural networks
Byravan et al. Graph-Based Inverse Optimal Control for Robot Manipulation.
Toussaint et al. A bayesian view on motor control and planning
Kappler et al. A new data source for inverse dynamics learning
Millard et al. Automatic differentiation and continuous sensitivity analysis of rigid body dynamics
Alt et al. Robot program parameter inference via differentiable shadow program inversion
Ghalyan Force-Controlled Robotic Assembly Processes of Rigid and Flexible Objects
Mitsioni et al. Safe data-driven contact-rich manipulation
Tanwani et al. Generalizing robot imitation learning with invariant hidden semi-Markov models
Ordoñez-Apraez et al. Morphological symmetries in robotics
Sherikov Balance preservation and task prioritization in whole body motion control of humanoid robots