This paper introduces the task of Planar Robot Casting (PRC): where one planar motion of a robot arm holding one end of a cable causes the other end to slide across the plane toward a desired target. PRC allows the cable to reach points beyond the robot workspace and has applications for cable management in homes, warehouses, and factories. To efficiently learn a PRC policy for a given cable, we propose Real2Sim2Real, a self-supervised framework that automatically collects physical trajectory examples to tune parameters of a dynamics simulator using Differential Evolution, generates many simulated examples, and then learns a policy using a weighted combination of simulated and physical data. We evaluate Real2Sim2Real with three simulators, Isaac Gym-segmented, Isaac Gym-hybrid, and PyBullet, two function approximators, Gaussian Processes and Neural Networks (NNs), and three cables with differing stiffness, torsion, and friction. Results with 240 physical trials suggest that the PRC policies can attain median error distance (as % of cable length) ranging from 8 % to 14 %, outperforming baselines and policies trained on only real or only simulated examples. Code, data, and videos are available at https://tinyurl.com/robotcast.

The authors seek to identify the primary factors that determine the time-to-recovery of a network of DOCSIS-compliant cable modems (CMs). DOCSIS, which stands for Data-Over-Cable Service Interface Specifications (L.P. Holdings, 1999), is an emerging industry standard for data services over the cable TV infrastructure. According to the specification, individual cable modems must compete for access to the upstream channel, making it difficult to predict how long it will take for all CMs to initialize. We have developed a high fidelity simulation of the DOCSIS 1.1 upstream channel which allows us to test various upstream channel allocation rules. We have simulated networks of 200, 500, and 1000 CMs, and we have used the results to estimate the distribution function for the time-to-initialize for each topology. We have found that there is a tradeoff between optimizing the initialization process and the steady state operation of the network. In particular, allocation rules that reserve channel capacity for initialization lead to shorter initialization times at the expense of reduced capacity for the transmission of data.