Dyadic collaborative manipulation formalism for optimizing human-robot teaming

Dyadic collaborative Manipulation (DcM) is a term we use to refer to a team of two individuals, the agent and the partner, jointly manipulating an object. The two individuals partner together to form a distributed system, augmenting their manipulation abilities. Effective collaboration between the two individuals during joint action depends on: (i) the breadth of the agent’s action repertoire, (ii) the level of model acquaintance between the two individuals, (iii) the ability to adapt online of one’s own actions to the actions of their partner, and (iv) the ability to estimate the partner’s intentions and goals. Key to the successful completion of co-manipulation tasks with changing goals is the agent’s ability to change grasp-holds, especially in large object co-manipulation scenarios. Hence, in this work we developed a Trajectory Optimization (TO) method to enhance the repertoire of actions of robotic agents, by enabling them to plan and execute hybrid motions, i.e. motions that include discrete contact transitions, continuous trajectories and force profiles. The effectiveness of the TO method is investigated numerically and in simulation, in a number of manipulation scenarios with both a single and a bimanual robot. In addition, it is worth noting that transitions from free motion to contact is a challenging problem in robotics, in part due to its hybrid nature. Additionally, disregarding the effects of impacts at the motion planning level often results in intractable impulsive contact forces. To address this challenge, we introduce an impact-aware multi-mode TO method that combines hybrid dynamics and hybrid control in a coherent fashion. A key concept in our approach is the incorporation of an explicit contact force transmission model into the TO method. This allows the simultaneous optimization of the contact forces, contact timings, continuous motion trajectories and compliance, while satisfying task constraints. To demonstrate the benefits of our method, we compared our method against standard compliance control and an impact-agnostic TO method in physical simulations. Also, we experimentally validated the proposed method with a robot manipulator on the task of halting a large-momentum object. Further, we propose a principled formalism to address the joint planning problem in DcM scenarios and we solve the joint problem holistically via model-based optimization by representing the human's behavior as task space forces. The task of finding the partner-aware contact points, forces and the respective timing of grasp-hold changes are carried out by a TO method using non-linear programming. Using simulations, the capability of the optimization method is investigated in terms of robot policy changes (trajectories, timings, grasp-holds) to potential changes of the collaborative partner policies. We also realized, in hardware, effective co-manipulation of a large object by the human and the robot, including eminent grasp changes as well as optimal dyadic interactions to realize the joint task. To address the online adaptation challenge of joint motion plans in dyads, we propose an efficient bilevel formulation which combines graph search methods with trajectory optimization, enabling robotic agents to adapt their policy on-the-fly in accordance to changes of the dyadic task. This method is the first to empower agents with the ability to plan online in hybrid spaces; optimizing over discrete contact locations, contact sequence patterns, continuous trajectories, and force profiles for co-manipulation tasks. This is particularly important in large object co-manipulation tasks that require on-the-fly plan adaptation. We demonstrate in simulation and with robot experiments the efficacy of the bilevel optimization by investigating the effect of robot policy changes in response to real-time alterations of the goal. This thesis provides insight into joint manipulation setups performed by human-robot teams. In particular, it studies computational models of joint action and exploits the uncharted hybrid action space, that is especially relevant in general manipulation and co-manipulation tasks. It contributes towards developing a framework for DcM, capable of planning motions in the contact-force space, realizing these motions while considering impacts and joint action relations, as well as adapting on-the-fly these motion plans with respect to changes of the co-manipulation goals

A Behavioural Transformer for Effective Collaboration between a Robot and a Non-stationary Human

A key challenge in human-robot collaboration is the non-stationarity created by humans due to changes in their behaviour. This alters environmental transitions and hinders human-robot collaboration. We propose a principled meta-learning framework to explore how robots could better predict human behaviour, and thereby deal with issues of non-stationarity. On the basis of this framework, we developed Behaviour-Transform (BeTrans). BeTrans is a conditional transformer that enables a robot agent to adapt quickly to new human agents with non-stationary behaviours, due to its notable performance with sequential data. We trained BeTrans on simulated human agents with different systematic biases in collaborative settings. We used an original customisable environment to show that BeTrans effectively collaborates with simulated human agents and adapts faster to non-stationary simulated human agents than SOTA techniques

Decentralized Ability-Aware Adaptive Control for Multi-robot Collaborative Manipulation

Multi-robot teams can achieve more dexterous, complex and heavier payload tasks than a single robot, yet effective collaboration is required. Multi-robot collaboration is extremely challenging due to the different kinematic and dynamics capabilities of the robots, the limited communication between them, and the uncertainty of the system parameters. In this paper, a Decentralized Ability-Aware Adaptive Control is proposed to address these challenges based on two key features. Firstly, the common manipulation task is represented by the proposed nominal task ellipsoid, which is used to maximize each robot force capability online via optimizing its configuration. Secondly, a decentralized adaptive controller is designed to be Lyapunov stable in spite of heterogeneous actuation constraints of the robots and uncertain physical parameters of the object and environment. In the proposed framework, decentralized coordination and load distribution between the robots is achieved without communication, while only the control deficiency is broadcast if any of the robots reaches its force limits. In this case, the object reference trajectory is modified in a decentralized manner to guarantee stable interaction. Finally, we perform several numerical and physical simulations to analyse and verify the proposed method with heterogeneous multi-robot teams in collaborative manipulation tasks.Comment: The article has been submitted to IEEE Robotics and Automation Letters (RA-L) with ICRA 2021 conference option; the article has been accepted for publication in RA-

Non-prehensile Planar Manipulation via Trajectory Optimization with Complementarity Constraints

Contact adaption is an essential capability when manipulating objects. Two key contact modes of non-prehensile manipulation are sticking and sliding. This paper presents a Trajectory Optimization (TO) method formulated as a Mathematical Program with Complementarity Constraints (MPCC), which is able to switch between these two modes. We show that this formulation can be applicable to both planning and Model Predictive Control (MPC) for planar manipulation tasks. We numerically compare: (i) our planner against a mixed integer alternative, showing that the MPCC planer converges faster, scales better with respect to time horizon, and can handle environments with obstacles; (ii) our controller against a state-of-the-art mixed integer approach, showing that the MPCC controller achieves better tracking and more consistent computation times. Additionally, we experimentally validate both our planner and controller with the KUKA LWR robot on a range of planar manipulation tasks

Multi-mode Trajectory Optimization for Impact-aware Manipulation

The transition from free motion to contact is a challenging problem in robotics, in part due to its hybrid nature. Additionally, disregarding the effects of impacts at the motion planning level often results in intractable impulsive contact forces. In this paper, we introduce an impact-aware multi-mode trajectory optimization (TO) method that combines hybrid dynamics and hybrid control in a coherent fashion. A key concept is the incorporation of an explicit contact force transmission model in the TO method. This allows the simultaneous optimization of the contact forces, contact timings, continuous motion trajectories and compliance, while satisfying task constraints. We compare our method against standard compliance control and an impact-agnostic TO method in physical simulations. Further, we experimentally validate the proposed method with a robot manipulator on the task of halting a large-momentum object

Set-based State Estimation with Probabilistic Consistency Guarantee under Epistemic Uncertainty

Consistent state estimation is challenging, especially under the epistemic uncertainties arising from learned (nonlinear) dynamic and observation models. In this work, we propose a set-based estimation algorithm, named Gaussian Process-Zonotopic Kalman Filter (GP-ZKF), that produces zonotopic state estimates while respecting both the epistemic uncertainties in the learned models and aleatoric uncertainties. Our method guarantees probabilistic consistency, in the sense that the true states are bounded by sets (zonotopes) across all time steps, with high probability. We formally relate GP-ZKF with the corresponding stochastic approach, GP-EKF, in the case of learned (nonlinear) models. In particular, when linearization errors and aleatoric uncertainties are omitted and epistemic uncertainties are simplified, GP-ZKF reduces to GP-EKF. We empirically demonstrate our method's efficacy in both a simulated pendulum domain and a real-world robot-assisted dressing domain, where GP-ZKF produced more consistent and less conservative set-based estimates than all baseline stochastic methods.Comment: Published at IEEE Robotics and Automation Letters, 2022. Video: https://www.youtube.com/watch?v=CvIPJlALaFU Copyright: 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any media, including reprinting/republishing for any purposes, creating new works, for resale or redistribution, or reuse of any copyrighted component of this wor