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Found 17 papers, 4 papers with code
Intention-Aware Control Based on Belief-Space Specifications and Stochastic Expansion
This paper develops a correct-by-design controller for an autonomous vehicle interacting with opponent vehicles with unknown intentions.
Risk-Aware Real-Time Task Allocation for Stochastic Multi-Agent Systems under STL Specifications
This paper addresses the control synthesis of heterogeneous stochastic linear multi-agent systems with real-time allocation of signal temporal logic (STL) specifications.
Risk-Aware MPC for Stochastic Systems with Runtime Temporal Logics
This paper concerns the risk-aware control of stochastic systems with temporal logic specifications dynamically assigned during runtime.
Identifying Reaction-Aware Driving Styles of Stochastic Model Predictive Controlled Vehicles by Inverse Reinforcement Learning
Nevertheless, an important indicator of the driving style, i. e., how an AV reacts to its nearby AVs, is not fully incorporated in the feature design of previous ME-IRL methods.
Using Implicit Behavior Cloning and Dynamic Movement Primitive to Facilitate Reinforcement Learning for Robot Motion Planning
Reinforcement learning (RL) for motion planning of multi-degree-of-freedom robots still suffers from low efficiency in terms of slow training speed and poor generalizability.
Risk-Aware Reward Shaping of Reinforcement Learning Agents for Autonomous Driving
Reinforcement learning (RL) is an effective approach to motion planning in autonomous driving, where an optimal driving policy can be automatically learned using the interaction data with the environment.
Human-Robot Skill Transfer with Enhanced Compliance via Dynamic Movement Primitives
Our contribution is introducing a systematic method to extract the dynamic features from human demonstration to auto-tune the parameters in the DMP framework.
Distributed Coverage Control of Constrained Constant-Speed Unicycle Multi-Agent Systems
This paper proposes a novel distributed coverage controller for a multi-agent system with constant-speed unicycle robots (CSUR).
A Persistent-Excitation-Free Method for System Disturbance Estimation Using Concurrent Learning
A simulation study on epidemic control shows that the proposed method produces higher estimation precision than the conventional disturbance observer when PE is not satisfied.
Facilitating Sim-to-real by Intrinsic Stochasticity of Real-Time Simulation in Reinforcement Learning for Robot Manipulation
Simulation is essential to reinforcement learning (RL) before implementation in the real world, especially for safety-critical applications like robot manipulation.
Exploiting Symmetry and Heuristic Demonstrations in Off-policy Reinforcement Learning for Robotic Manipulation
A comparison study between the proposed method and a traditional off-policy reinforcement learning algorithm indicates its advantage in learning performance and potential value for applications.
Automated Formation Control Synthesis from Temporal Logic Specifications
In many practical scenarios, multi-robot systems are envisioned to support humans in executing complicated tasks within structured environments, such as search-and-rescue tasks.
Modularized Control Synthesis for Complex Signal Temporal Logic Specifications
In this paper, we propose a framework to transform a long and complex specification into separate forms in time, to be more specific, the logical combination of a series of short and simple subformulas with non-overlapping timing intervals.
Simultaneous Recursive Identification of Parameters and Switching Manifolds Identification of Discrete-Time Switched Linear Systems
Thirdly, techniques of incremental support vector machine are applied to develop the recursive algorithm to estimate the system switching manifolds, with its stability proven by a Lynapunov-based method.
Average Communication Rate for Event-Triggered Stochastic Control Systems
Quantifying the average communication rate (ACR) of a networked event-triggered stochastic control system (NET-SCS) with deterministic thresholds is challenging due to the non-stationary nature of the system's stochastic processes.
Adaptive Observer for a Class of Systems with Switched Unknown Parameters Using DREM
These responses depend on the unknown states at switching instants (SASI) and constitute an additive disturbance to the parameter estimation, which obstructs parameter convergence to zero.
Instantaneous Local Control Barrier Function: An Online Learning Approach for Collision Avoidance
This paper presents an integrated perception and control approach to accomplish safe autonomous navigation in unknown environments.
