Robots that can physically interact with humans in a safe manner have the potential to revolutionize application domains like home assistance and nursing care. However, to become long-term companions, such robots must learn user-specific preferences and adapt their behaviors in real time. We propose a Constrained Partially Observable Markov Decision Process framework for modeling human safety preferences over representative variables like force, velocity, and proximity. These variables are modeled as adaptive linear constraints, with a belief over their upper bounds that is updated online based on noisy human feedback. By modeling the belief as phase dependent, the model captures varying preferences across different task phases. The robot then solves a hierarchical optimization to select actions that respect both the learned constraints and robot motion limits. Our method does not require offline training data and can be applied directly to diverse physical interaction tasks and operation modes (tele-operated or autonomous). A pilot study shows that our approach effectively learns user preferences and improves perceived safety while reducing user effort compared to baselines.

This paper introduces a new method for safety-aware robot learning, focusing on repairing policies using predictive models. Our method combines behavioral cloning with neural network repair in a two-step supervised learning framework. It first learns a policy from expert demonstrations and then applies repair subject to predictive models to enforce safety constraints. The predictive models can encompass various aspects relevant to robot learning applications, such as proprioceptive states and collision likelihood. Our experimental results demonstrate that the learned policy successfully adheres to a predefined set of safety constraints on two applications: mobile robot navigation, and real-world lower-leg prostheses. Additionally, we have shown that our method effectively reduces repeated interaction with the robot, leading to substantial time savings during the learning process.

The increasing deployment of robots in co-working scenarios with humans has revealed complex safety and efficiency challenges in the computation of the robot behavior. Movement among humans is one of the most fundamental —and yet critical—problems in this frontier. While several approaches have addressed this problem from a purely navigational point of view, the absence of a unified paradigm for communicating with humans limits their ability to prevent deadlocks and compute feasible solutions. This paper presents a joint communication and motion planning framework that selects from an arbitrary input set of robot's communication signals while computing robot motion plans. It models a human co-worker's imperfect perception of these communications using a noisy sensor model and facilitates the specification of a variety of social/workplace compliance priorities with a flexible cost function. Theoretical results and simulator-based empirical evaluations show that our approach efficiently computes motion plans and communication strategies that reduce conflicts between agents and resolve potential deadlocks.

Sampling-based methods such as Rapidly-exploring Random Trees (RRTs) have been widely used for generating motion paths for autonomous mobile systems. In this work, we extend time-based RRTs with Control Barrier Functions (CBFs) to generate, safe motion plans in dynamic environments with many pedestrians. Our framework is based upon a human motion prediction model which is well suited for indoor narrow environments. We demonstrate our approach on a high-fidelity model of the Toyota Human Support Robot navigating in narrow corridors. We show in simulation results that our proposed online method can navigate safely in the presence of moving agents with unknown dynamics.

In this paper, a novel kinematic-based optimal trajectory planning formulation for an autonomous vehicle is presented. In this new formulation, the quadratic errors of position, velocity, and acceleration are minimized subject to the rear wheel car-like vehicle nonlinear kinematic model. Minimizing the error of velocity and acceleration in addition to the error of position, allows us to obtain both optimal vehicle trajectory and control law. The Variational approach is used to minimize the cost function. Then, optimal trajectory and control inputs are numerically calculated by solving a set of two-point boundary value (TPBV) nonlinear differential equations. Finally, the proposed method is evaluated in two scenarios of lane changing and multi-curvature road which verify the success of the proposed method in generating an optimal trajectory and control inputs.