Guang Yang

Bio: Guang Yang is an academic researcher from Boston University. The author has contributed to research in topics: Motion planning & Robotic arm. The author has an hindex of 6, co-authored 13 publications receiving 125 citations.

A formal methods approach to interpretable reinforcement learning for robotic planning.

Abstract: Growing interest in reinforcement learning approaches to robotic planning and control raises concerns of predictability and safety of robot behaviors realized solely through learned control policies. In addition, formally defining reward functions for complex tasks is challenging, and faulty rewards are prone to exploitation by the learning agent. Here, we propose a formal methods approach to reinforcement learning that (i) provides a formal specification language that integrates high-level, rich, task specifications with a priori, domain-specific knowledge; (ii) makes the reward generation process easily interpretable; (iii) guides the policy generation process according to the specification; and (iv) guarantees the satisfaction of the (critical) safety component of the specification. The main ingredients of our computational framework are a predicate temporal logic specifically tailored for robotic tasks and an automaton-guided, safe reinforcement learning algorithm based on control barrier functions. Although the proposed framework is quite general, we motivate it and illustrate it experimentally for a robotic cooking task, in which two manipulators worked together to make hot dogs.

Self-triggered Control for Safety Critical Systems Using Control Barrier Functions

Abstract: We propose a real-time control strategy that combines self-triggered control with Control Lyapunov Functions (CLF) and Control Barrier Functions (CBF). Similar to related works proposing CLF-CBF-based controllers, the computation of the controller is achieved by solving a Quadratic Program (QP). However, we propose a Zeroth-Order Hold (ZOH) implementation of the controller that overcomes the main limitations of traditional approaches based on periodic controllers, i.e., unnecessary controller updates and potential violations of the safety constraints. Central to our approach is the novel notion of safe period, which enforces a strong safety guarantee for implementing ZOH control. In addition, we prove that the system does not exhibit a Zeno behavior as it approaches the desired equilibrium.

OuijaBots: Omnidirectional Robots for Cooperative Object Transport with Rotation Control Using No Communication

Abstract: We propose a distributed force and torque controller for a group of robots to collectively transport objects with both translation and rotation control. No explicit communication among robots is required. This work goes beyond previous works by including rotation control and experimental demonstrations on a custom built robot platform. We prove that follower robots can synchronize both their forces and torques to a leader (either a robot or human) that guides the group, and thus contribute positively to the transport. We introduce a custom-designed omnidirectional robot platform, called the OuijaBot, with sensing and actuation capabilities for cooperative manipulation. Our approach is verified by experiments with four OuijaBots successfully transporting and rotating a payload through a narrow corridor.

Sampling-based Motion Planning via Control Barrier Functions

Abstract: Robot motion planning is central to real-world autonomous applications, such as self-driving cars, persistence surveillance, and robotic arm manipulation. One challenge in motion planning is generating control signals for nonlinear systems that result in obstacle free paths through dynamic environments. In this paper, we propose Control Barrier Function guided Rapidly-exploring Random Trees (CBF-RRT), a sampling-based motion planning algorithm for continuoustime nonlinear systems in dynamic environments. The algorithm focuses on two objectives: efficiently generating feasible controls that steer the system toward a goal region, and handling environments with dynamical obstacles in continuous time. We formulate the control synthesis problem as a Quadratic Program (QP) that enforces Control Barrier Function (CBF) constraints to achieve obstacle avoidance. Additionally, CBF-RRT does not require nearest neighbor or explicit collision checks during sampling.

Continuous-time Signal Temporal Logic Planning with Control Barrier Functions.

Abstract: Temporal Logic (TL) guided control problems have gained enormous interests in recent years. A wide range of properties, such as liveness and safety, can be specified through TL. On the other hand, Control Barrier Functions (CBF) have shown success in the context of safety critical applications that require constraints on the system states. In this paper, we consider linear cyber-physical systems with continuous dynamics, where controls are generated by digital computers in discrete time. We propose an offline trajectory planner for such systems subject to linear constraints given as Signal Temporal Logic (STL) formulas. The proposed planner is based on a Mixed Integer Quadratic Programming (MIQP) formulation that utilizes CBFs to produce system trajectories that are valid in continuous time; moreover we allow STL predicates with arbitrary time constraints, in which asynchronous control updates are allowed. We validate our theoretical results through numerical simulations.

Cooperative Object Transport in Multi-robot Systems: A Review of the State-of-the-Art

Abstract: In recent years, there has been a growing interest in designing multi-robot systems (hereafter MRSs) to provide cost effective, fault-tolerant and reliable solutions to a variety of automated applications. Here, we review recent advancements in MRSs specifically designed for cooperative object transport, which requires the members of MRSs to coordinate their actions to transport objects from a starting position to a final destination. To achieve cooperative object transport, a wide range of transport, coordination and control strategies have been proposed. Our goal is to provide a comprehensive summary for this relatively heterogeneous and fast-growing body of scientific literature. While distilling the information, we purposefully avoid using hierarchical dichotomies, which have been traditionally used in the field of MRSs. Instead, we employ a coarse-grain approach by classifying each study based on the transport strategy used; pushing-only, grasping and caging. We identify key design constraints that may be shared among these studies despite considerable differences in their design methods. In the end, we discuss several open challenges and possible directions for future work to improve the performance of the current MRSs. Overall, we hope to increasethe visibility and accessibility of the excellent studies in the field and provide a framework that helps the reader to navigate through them more effectively.

Intercepting Rogue Robots: An Algorithm for Capturing Multiple Evaders With Multiple Pursuers

Abstract: We propose a distributed algorithm for the cooperative pursuit of multiple evaders using multiple pursuers in a bounded convex environment. The algorithm is suitable for intercepting rogue drones in protected airspace, among other applications. The pursuers do not know the evaders' policy, but by using a global “area-minimization” strategy based on a Voronoi tessellation of the environment, we guarantee the capture of all evaders in finite time. We present a decentralized version of this policy applicable in two-dimensional (2-D) and 3-D environments, and show in multiple simulations that it outperforms other decentralized multipursuer heuristics. Experiments with both autonomous and human-controlled robots were conducted to demonstrate the practicality of the approach. Specifically, human-controlled evaders are not able to avoid capture with the algorithm.

Reward Machines: Exploiting Reward Function Structure in Reinforcement Learning.

Abstract: Reinforcement learning (RL) methods usually treat reward functions as black boxes. As such, these methods must extensively interact with the environment in order to discover rewards and optimal policies. In most RL applications, however, users have to program the reward function and, hence, there is the opportunity to treat reward functions as white boxes instead -- to show the reward function's code to the RL agent so it can exploit its internal structures to learn optimal policies faster. In this paper, we show how to accomplish this idea in two steps. First, we propose reward machines (RMs), a type of finite state machine that supports the specification of reward functions while exposing reward function structure. We then describe different methodologies to exploit such structures, including automated reward shaping, task decomposition, and counterfactual reasoning for data augmentation. Experiments on tabular and continuous domains show the benefits of exploiting reward structure across different tasks and RL agents.

Robust collaborative object transportation using multiple MAVs

Abstract: Collaborative object transportation using multiple MAV with limited communication is a challenging problem. In this paper, we address the problem of multiple MAV mechanically coupled to a bulky obj...

Decentralized Adaptive Control for Collaborative Manipulation

Abstract: This paper presents a design for a decentralized adaptive controller that allows a team of agents to manipulate a common payload in $\mathbb{R}^{2}$ or $\mathbb{R}^{3}$ The controller requires no communication between agents and requires no a priori knowledge of agent positions or payload properties The agents can control the payload to track a reference trajectory in linear and angular velocity with center-of-mass measurements, in angular velocity using only local measurements and a common frame, and can stabilize its rotation with only local measurements The controller is designed via a Lyapunov-style analysis and has proven stability and convergence The controller is validated in simulation and experimentally with four robots manipulating an object in the plane