Factory: Fast Contact for Robotic Assembly

Cable routing is a challenging task for robotic automation. To accomplish the task, it requires a high-level path planner to generate a sequence of cable configurations from the initial state to the target state and a low-level manipulation planner to plan the robot motion commands to transit between adjacent states. However, there are yet no proper representations to model the cable with the environment objects, impeding the design of both high-level path planning and low-level manipulation planning. In this letter, we propose a framework for cable routing with spatial representation. For high-level planning, by considering the spatial relations between the cable and the environment objects such as fixtures, the proposed method is able to plan a path from the initial state to the goal state in a graph. For low-level manipulation, multiple manipulation primitives are efficiently learned from human demonstration, to configure the cable to planned intermediate states leveraging the same spatial representation. We also implement a cable state estimator that robustly extracts the spatial representation from raw RGB-D images, thus completing the cable routing framework. We evaluate the proposed framework with various cables and fixture settings, and demonstrate that it outperforms some baselines in terms of reliability and generalizability.

Robotic Cable Routing with Spatial Representation

Robotic manipulation of deformable linear objects (DLOs) has broad application prospects in many fields. However, a key issue is to obtain the exact deformation models (i.e., how robot motion affects DLO deformation), which are hard to theoretically calculate and vary among different DLOs. Thus, shape control of DLOs is challenging, especially for large deformation control which requires global and more accurate models. In this paper, we propose a coupled offline and online data-driven method for efficiently learning a global deformation model, allowing for both accurate modeling through offline learning and further updating for new DLOs via online adaptation. Specifically, the model approximated by a neural network is first trained offline on random data, then seamlessly migrated to the online phase, and further updated online during actual manipulation. Several strategies are introduced to improve the model's efficiency and generalization ability. We propose a convex-optimization-based controller, and analyze the system's stability using the Lyapunov method. Detailed simulations and real-world experiments demonstrate that our method can efficiently and precisely estimate the deformation model, and achieve large deformation control of untrained DLOs in 2D and 3D dual-arm manipulation tasks better than the existing methods. It accomplishes all 24 tasks with different desired shapes on different DLOs in the real world, using only simulation data for the offline learning. 

/pdf/global-model-learning-for-large-deformation-control-of-16ahd03u.pdf

Global Model Learning for Large Deformation Control of Elastic Deformable Linear Objects: An Efficient and Adaptive Approach

The shape control of deformable linear objects (DLOs) is challenging, since
it is difficult to obtain the deformation models. Previous studies often
approximate the models in purely offline or online ways. In this paper, we
propose a scheme for the shape control of DLOs, where the unknown model is
estimated with both offline and online learning. The model is formulated in a
local linear format, and approximated by a neural network (NN). First, the NN
is trained offline to provide a good initial estimation of the model, which can
directly migrate to the online phase. Then, an adaptive controller is proposed
to achieve the shape control tasks, in which the NN is further updated online
to compensate for any errors in the offline model caused by insufficient
training or changes of DLO properties. The simulation and real-world
experiments show that the proposed method can precisely and efficiently
accomplish the DLO shape control tasks, and adapt well to new and untrained
DLOs.

Shape Control of Deformable Linear Objects with Offline and Online Learning of Local Linear Deformation Models

This paper tackles the task of goal-conditioned dynamic manipulation of deformable objects. This task is highly challenging due to its complex dynamics (introduced by object deformation and high-speed action) and strict task requirements (defined by a precise goal specification). To address these challenges, we present Iterative Residual Policy (IRP), a general learning framework applicable to repeatable tasks with complex dynamics. IRP learns an implicit policy via delta dynamics -- instead of modeling the entire dynamical system and inferring actions from that model, IRP learns delta dynamics that predict the effects of delta action on the previously-observed trajectory. When combined with adaptive action sampling, the system can quickly optimize its actions online to reach a specified goal. We demonstrate the effectiveness of IRP on two tasks: whipping a rope to hit a target point and swinging a cloth to reach a target pose. Despite being trained only in simulation on a fixed robot setup, IRP is able to efficiently generalize to noisy real-world dynamics, new objects with unseen physical properties, and even different robot hardware embodiments, demonstrating its excellent generalization capability relative to alternative approaches. Video is available at https://youtu.be/7h3SZ3La-oA 

/pdf/iterative-residual-policy-for-goal-conditioned-dynamic-22ee3xo1.pdf

Iterative Residual Policy for Goal-Conditioned Dynamic Manipulation of Deformable Objects

This paper presents a novel trajectory optimization formulation to solve the robotic assembly of the belt drive unit. Robotic manipulations involving contacts and deformable objects are challenging in both dynamic modeling and trajectory planning. For modeling, variations in the belt tension and contact forces between the belt and the pulley could dramatically change the system dynamics. For trajectory planning, it is computationally expensive to plan trajectories for such hybrid dynamical systems as it usually requires planning for discrete modes separately. In this work, we formulate the belt drive unit assembly task as a trajectory optimization problem with complementarity constraints to avoid explicitly imposing contact mode sequences. The problem is solved as a mathematical program with complementarity constraints (MPCC) to obtain feasible and efficient assembly trajectories. We validate the proposed method both in simulations with a physics engine and in real-world experiments with a robotic manipulator.

Trajectory Optimization for Manipulation of Deformable Objects: Assembly of Belt Drive Units

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Trajectory Optimization for Manipulation of Deformable Objects: Assembly of Belt Drive Units

Trajectory Optimization for Manipulation of Deformable Objects: Assembly of Belt Drive Units