Constrained Passive Interaction Control: Leveraging Passivity and Safety for Robot Manipulators

Passivity is necessary for robots to fluidly collaborate and interact with humans physically. Nevertheless, due to the unconstrained nature of passivity-based impedance control laws, the robot is vulnerable to infeasible and unsafe configurations upon physical perturbations. In this paper, we propose a novel control architecture that allows a torque-controlled robot to guarantee safety constraints such as kinematic limits, self-collisions, external collisions and singularities and is passive only when feasible. This is achieved by constraining a dynamical system based impedance control law with a relaxed hierarchical control barrier function quadratic program subject to multiple concurrent, possibly contradicting, constraints. Joint space constraints are formulated from efficient data-driven self- and external C^2 collision boundary functions. We theoretically prove constraint satisfaction and show that the robot is passive when feasible. Our approach is validated in simulation and real robot experiments on a 7DoF Franka Research 3 manipulator.

Realistic Safety-critical Scenarios Search for Autonomous Driving System via Behavior Tree

The simulation-based testing of Autonomous Driving Systems (ADSs) has gained significant attention. However, current approaches often fall short of accurately assessing ADSs for two reasons: over-reliance on expert knowledge and the utilization of simplistic evaluation metrics. That leads to discrepancies between simulated scenarios and naturalistic driving environments. To address this, we propose the Matrix-Fuzzer, a behavior tree-based testing framework, to automatically generate realistic safety-critical test scenarios. Our approach involves the $log2BT$ method, which abstracts logged road-users' trajectories to behavior sequences. Furthermore, we vary the properties of behaviors from real-world driving distributions and then use an adaptive algorithm to explore the input space. Meanwhile, we design a general evaluation engine that guides the algorithm toward critical areas, thus reducing the generation of invalid scenarios. Our approach is demonstrated in our Matrix Simulator. The experimental results show that: (1) Our $log2BT$ achieves satisfactory trajectory reconstructions. (2) Our approach is able to find the most types of safety-critical scenarios, but only generating around 30% of the total scenarios compared with the baseline algorithm. Specifically, it improves the ratio of the critical violations to total scenarios and the ratio of the types to total scenarios by at least 10x and 5x, respectively, while reducing the ratio of the invalid scenarios to total scenarios by at least 58% in two case studies.