He et al., 2021 - Google Patents
A Barrier Pair Method for Safe Human-Robot Shared AutonomyHe et al., 2021
View PDF- Document ID
- 1936723735571819101
- Author
- He B
- Ghasemi M
- Topcu U
- Sentis L
- Publication year
- Publication venue
- 2021 60th IEEE Conference on Decision and Control (CDC)
External Links
Snippet
Shared autonomy provides a framework where a human and an automated system, such as a robot, jointly control the system's behavior, enabling an effective solution for various applications, including human-robot interaction. However, a challenging problem in shared …
- 230000004888 barrier function 0 abstract description 15
Classifications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
- B25J9/1602—Programme controls characterised by the control system, structure, architecture
- B25J9/1607—Calculation of inertia, jacobian matrixes and inverses
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/0265—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
- G05B13/027—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion using neural networks only
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/30—Nc systems
- G05B2219/39—Robotics, robotics to robotics hand
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
- B25J9/1674—Programme controls characterised by safety, monitoring, diagnostic
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Bhardwaj et al. | Storm: An integrated framework for fast joint-space model-predictive control for reactive manipulation | |
| Kebria et al. | Adaptive type-2 fuzzy neural-network control for teleoperation systems with delay and uncertainties | |
| Rozo et al. | Learning optimal controllers in human-robot cooperative transportation tasks with position and force constraints | |
| Castillo et al. | Intelligent adaptive model-based control of robotic dynamic systems with a hybrid fuzzy-neural approach | |
| Jamwal et al. | Forward kinematics modelling of a parallel ankle rehabilitation robot using modified fuzzy inference | |
| EP4019207A1 (en) | Model generation device, model generation method, control device, and control method | |
| Ravichandar et al. | Learning contracting nonlinear dynamics from human demonstration for robot motion planning | |
| Rezaei-Shoshtari et al. | Cascaded gaussian processes for data-efficient robot dynamics learning | |
| Mathew et al. | Online learning of feed-forward models for task-space variable impedance control | |
| Pane et al. | Autonomous runtime composition of sensor-based skills using concurrent task planning | |
| Mielke et al. | Human-robot co-manipulation of extended objects: Data-driven models and control from analysis of human-human dyads | |
| Ahmad et al. | Learning to adapt the parameters of behavior trees and motion generators (btmgs) to task variations | |
| Michaux et al. | Can't Touch This: Real-Time, Safe Motion Planning and Control for Manipulators Under Uncertainty | |
| He et al. | A Barrier Pair Method for Safe Human-Robot Shared Autonomy | |
| He et al. | A distributed optimal control framework for multi-robot cooperative manipulation in dynamic environments | |
| Kolaric et al. | Local policy optimization for trajectory-centric reinforcement learning | |
| Mamedov et al. | Safe Imitation Learning of Nonlinear Model Predictive Control for Flexible Robots | |
| Katayama et al. | Efficient solution method based on inverse dynamics for optimal control problems of rigid body systems | |
| Adu et al. | Bring the heat: Rapid trajectory optimization with pseudospectral techniques and the affine geometric heat flow equation | |
| Hejrati et al. | Decentralized nonlinear control of redundant upper limb exoskeleton with natural adaptation law | |
| Oikonomou et al. | Task driven skill learning in a soft-robotic arm | |
| Ewerton et al. | Reinforcement learning of trajectory distributions: Applications in assisted teleoperation and motion planning | |
| Sugimoto et al. | Trajectory-model-based reinforcement learning: Application to bimanual humanoid motor learning with a closed-chain constraint | |
| Vochten et al. | Shape-preserving and reactive adaptation of robot end-effector trajectories | |
| Jäkel et al. | Learning of probabilistic grasping strategies using programming by demonstration |