Demonstration-guided Optimal Control for Long-term Non-prehensile Planar Manipulation

Long-term non-prehensile planar manipulation is a challenging task for robot planning and feedback control. It is characterized by underactuation, hybrid control, and contact uncertainty. One main difficulty is to determine contact points and directions, which involves joint logic and geometrical reasoning in the modes of the dynamics model. To tackle this issue, we propose a demonstration-guided hierarchical optimization framework to achieve offline task and motion planning (TAMP). Our work extends the formulation of the dynamics model of the pusher-slider system to include separation mode with face switching cases, and solves a warm-started TAMP problem by exploiting human demonstrations. We show that our approach can cope well with the local minima problems currently present in the state-of-the-art solvers and determine a valid solution to the task. We validate our results in simulation and demonstrate its applicability on a pusher-slider system with real Franka Emika robot in the presence of external disturbances

Nonprehensile Planar Manipulation through Reinforcement Learning with Multimodal Categorical Exploration

Developing robot controllers capable of achieving dexterous nonprehensile manipulation, such as pushing an object on a table, is challenging. The underactuated and hybrid-dynamics nature of the problem, further complicated by the uncertainty resulting from the frictional interactions, requires sophisticated control behaviors. Reinforcement Learning (RL) is a powerful framework for developing such robot controllers. However, previous RL literature addressing the nonprehensile pushing task achieves low accuracy, non-smooth trajectories, and only simple motions, i.e. without rotation of the manipulated object. We conjecture that previously used unimodal exploration strategies fail to capture the inherent hybrid-dynamics of the task, arising from the different possible contact interaction modes between the robot and the object, such as sticking, sliding, and separation. In this work, we propose a multimodal exploration approach through categorical distributions, which enables us to train planar pushing RL policies for arbitrary starting and target object poses, i.e. positions and orientations, and with improved accuracy. We show that the learned policies are robust to external disturbances and observation noise, and scale to tasks with multiple pushers. Furthermore, we validate the transferability of the learned policies, trained entirely in simulation, to a physical robot hardware using the KUKA iiwa robot arm. See our supplemental video: https://youtu.be/vTdva1mgrk4

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

Nonprehensile Planar Manipulation through Reinforcement Learning with Multimodal Categorical Exploration

Developing robot controllers capable of achieving dexterous nonprehensile manipulation, such as pushing an object on a table, is challenging. The underactuated and hybrid-dynamics nature of the problem, further complicated by the uncertainty resulting from the frictional interactions, requires sophisticated control behaviors. Reinforcement Learning (RL) is a powerful framework for developing such robot controllers. However, previous RL literature addressing the nonprehensile pushing task achieves low accuracy, non-smooth trajectories, and only simple motions, i.e. without rotation of the manipulated object. We conjecture that previously used unimodal exploration strategies fail to capture the inherent hybrid-dynamics of the task, arising from the different possible contact interaction modes between the robot and the object, such as sticking, sliding, and separation. In this work, we propose a multimodal exploration approach through categorical distributions, which enables us to train planar pushing RL policies for arbitrary starting and target object poses, i.e. positions and orientations, and with improved accuracy. We show that the learned policies are robust to external disturbances and observation noise, and scale to tasks with multiple pushers. Furthermore, we validate the transferability of the learned policies, trained entirely in simulation, to a physical robot hardware using the KUKA iiwa robot arm. See our supplemental video: https://youtu.be/vTdva1mgrk4

Learning Pneumatic Non-Prehensile Manipulation with a Mobile Blower

We investigate pneumatic non-prehensile manipulation (i.e., blowing) as a means of efficiently moving scattered objects into a target receptacle. Due to the chaotic nature of aerodynamic forces, a blowing controller must (i) continually adapt to unexpected changes from its actions, (ii) maintain fine-grained control, since the slightest misstep can result in large unintended consequences (e.g., scatter objects already in a pile), and (iii) infer long-range plans (e.g., move the robot to strategic blowing locations). We tackle these challenges in the context of deep reinforcement learning, introducing a multi-frequency version of the spatial action maps framework. This allows for efficient learning of vision-based policies that effectively combine high-level planning and low-level closed-loop control for dynamic mobile manipulation. Experiments show that our system learns efficient behaviors for the task, demonstrating in particular that blowing achieves better downstream performance than pushing, and that our policies improve performance over baselines. Moreover, we show that our system naturally encourages emergent specialization between the different subpolicies spanning low-level fine-grained control and high-level planning. On a real mobile robot equipped with a miniature air blower, we show that our simulation-trained policies transfer well to a real environment and can generalize to novel objects.Comment: Project page: https://learning-dynamic-manipulation.cs.princeton.ed

Ball positioning in robotic billiards: a nonprehensile manipulation-based solution

The development and testing of a robotic system to play billiards is described in this paper. The last two decades have seen a number of developments in creating robots to play billiards. Although the designed systems have uccessfully incorporated the kinematics required for gameplay, a system level approach needed for accurate shot- making has not been realized. The current work considers the different aspects, like machine vision, dynamics, robot design and computational intelligence, and proposes, for the first time, a method based on robotic non-prehensile manipulation. High-speed video tracking is employed to determine the parameters of balls dynamics. Furthermore, three-dimensional impact models, involving ball spin and friction, are developed for different collisions. A three degree of freedom manipulator is designed and fabricated to execute shots. The design enables the manipulator to position the cue on the ball accurately and strike with controlled speeds. The manipulator is controlled from a PC via a microcontroller board. For a given table scenario, optimization is used to search the inverse dynamics space to find best parameters for the robotic shot maker. Experimental results show that a 90% potting accuracy and a 100–200 mm post-shot cue ball positioning accuracy has been achieved by the autonomous system

Push to know! -- Visuo-Tactile based Active Object Parameter Inference with Dual Differentiable Filtering

For robotic systems to interact with objects in dynamic environments, it is essential to perceive the physical properties of the objects such as shape, friction coefficient, mass, center of mass, and inertia. This not only eases selecting manipulation action but also ensures the task is performed as desired. However, estimating the physical properties of especially novel objects is a challenging problem, using either vision or tactile sensing. In this work, we propose a novel framework to estimate key object parameters using non-prehensile manipulation using vision and tactile sensing. Our proposed active dual differentiable filtering (ADDF) approach as part of our framework learns the object-robot interaction during non-prehensile object push to infer the object's parameters. Our proposed method enables the robotic system to employ vision and tactile information to interactively explore a novel object via non-prehensile object push. The novel proposed N-step active formulation within the differentiable filtering facilitates efficient learning of the object-robot interaction model and during inference by selecting the next best exploratory push actions (where to push? and how to push?). We extensively evaluated our framework in simulation and real-robotic scenarios, yielding superior performance to the state-of-the-art baseline.Comment: 8 pages. Accepted at IROS 202

Nonprehensile Dynamic Manipulation: A Survey

Nonprehensile dynamic manipulation can be reason- ably considered as the most complex manipulation task. It might be argued that such a task is still rather far from being fully solved and applied in robotics. This survey tries to collect the results reached so far by the research community about planning and control in the nonprehensile dynamic manipulation domain. A discussion about current open issues is addressed as well

Survey on model-based manipulation planning of deformable objects

A systematic overview on the subject of model-based manipulation planning of deformable objects is presented. Existing modelling techniques of volumetric, planar and linear deformable objects are described, emphasizing the different types of deformation. Planning strategies are categorized according to the type of manipulation goal: path planning, folding/unfolding, topology modifications and assembly. Most current contributions fit naturally into these categories, and thus the presented algorithms constitute an adequate basis for future developments.Preprin