Human-level autonomous driving is an ever-elusive goal, with planning and decision making - the cognitive functions that determine driving behavior - posing the greatest challenge. Despite a proliferation of promising approaches, progress is stifled by the difficulty of deploying experimental planners in naturalistic settings. In this work, we propose Lab2Car, an optimization-based wrapper that can take a trajectory sketch from an arbitrary motion planner and convert it to a safe, comfortable, dynamically feasible trajectory that the car can follow. This allows motion planners that do not provide such guarantees to be safely tested and optimized in real-world environments. We demonstrate the versatility of Lab2Car by using it to deploy a machine learning (ML) planner and a classical planner on self-driving cars in Las Vegas. The resulting systems handle challenging scenarios, such as cut-ins, overtaking, and yielding, in complex urban environments like casino pick-up/drop-off areas. Our work paves the way for quickly deploying and evaluating candidate motion planners in realistic settings, ensuring rapid iteration and accelerating progress towards human-level autonomy.

In this paper, we introduce the first published planner to drive a car in dense, urban traffic using Inverse Reinforcement Learning (IRL). Our planner, DriveIRL, generates a diverse set of trajectory proposals and scores them with a learned model. The best trajectory is tracked by our self-driving vehicle's low-level controller. We train our trajectory scoring model on a 500+ hour real-world dataset of expert driving demonstrations in Las Vegas within the maximum entropy IRL framework. DriveIRL's benefits include: a simple design due to only learning the trajectory scoring function, a flexible and relatively interpretable feature engineering approach, and strong real-world performance. We validated DriveIRL on the Las Vegas Strip and demonstrated fully autonomous driving in heavy traffic, including scenarios involving cut-ins, abrupt braking by the lead vehicle, and hotel pickup/dropoff zones. Our dataset, a part of nuPlan, has been released to the public to help further research in this area.

Traditional offline programming or teach pendant-based methods limit the collaboration capabilities of users and robots to cope with complex and changing industrial tasks. To address efficient robot manipulation in high-mix and low-volume tasks, especially for skillful tasks involving both trajectory and force control requirements, fast robot teaching and skill transferability are critical. Compared to manually dragging a heavy robot, or programming a trajectory by complex calculations, we believe that robots can efficiently learn skills from direct teaching through telemanipulation, and improve the skills based on optimization with sensory feedback. In aerospace engine, maintenance, repair, and operations, the surfaces of aerospace components are required to be masked by tapes. We propose a fast and intuitive telemanipulation-based method to teach a robot these masking skills and compare the performance of the proposed method with teach pendant-based methods among several users. This study aims to prove the efficiency and intuitiveness of the telemanipulation-based method proposed herein for enabling a robot to learn skillful and complex manipulation tasks.

In this paper, we propose SCALAR, a calibration method to simultaneously calibrate the kinematic parameters of a 6-DoF robot and the extrinsic parameters of a 2-D laser range finder (LRF) attached to the robot's flange. The calibration setup requires only a flat plate with two small holes carved on it at a known distance from each other and a sharp tool-tip attached to the robot's flange. The calibration is formulated as a nonlinear optimization problem where the laser and the tool-tip are used to provide the planar and distance constraints, and the optimization problem is solved using the Levenberg-Marquardt algorithm. We demonstrate through experiments that SCALAR can reduce the mean and the maximum tool position error from 0.44 to 0.19 mm and from 1.41 to 0.50 mm, respectively.

In many industrial robotics applications, such as spot-welding, spray-painting or drilling, the robot is required to visit successively multiple targets. The robot travel time among the targets is a significant component of the overall execution time. This travel time is in turn greatly affected by the order of visit of the targets, and by the robot configurations used to reach each target. Therefore, it is crucial to optimize these two elements, a problem known in the literature as the Robotic Task Sequencing Problem (RTSP). Our contribution in this paper is two-fold. First, we propose a fast, near-optimal, algorithm to solve RTSP. The key to our approach is to exploit the classical distinction between task space and configuration space, which, surprisingly, has been so far overlooked in the RTSP literature. Second, we provide an open-source implementation of the above algorithm, which has been carefully benchmarked to yield an efficient, ready-to-use, software solution. We discuss the relationship between RTSP and other Traveling Salesman Problem (TSP) variants, such as the Generalized Traveling Salesman Problem (GTSP), and show experimentally that our method finds motion sequences of the same quality but using several orders of magnitude less computation time than existing approaches.

Industrial robots are increasingly used in various applications where the robot accuracy becomes very important, hence calibrations of the robot's kinematic parameters and the measurement system's extrinsic parameters are required. However, the existing calibration approaches are either too cumbersome or require another expensive external measurement system such as laser tracker or measurement spinarm. In this paper, we propose SCALAR, a calibration method to simultaneously improve the kinematic parameters of a 6-DoF robot and the extrinsic parameters of a 2D Laser Range Finder (LRF) that is attached to the robot. Three flat planes are placed around the robot, and for each plane the robot moves to several poses such that the LRF's ray intersect the respective plane. Geometric planar constraints are then used to optimize the calibration parameters using Levenberg-Marquardt nonlinear optimization algorithm. We demonstrate through simulations that SCALAR can reduce the average position and orientation errors of the robot system from 14.6 mm and 4.05° to 0.09 mm and 0.02°.

Fine robotic assembly, in which the parts to be assembled are small and fragile and lie in an unstructured environment, is still out of reach of today's industrial robots. The main difficulties arise in the precise localization of the parts in an unstructured environment and the control of contact interactions. Our contribution in this paper is twofold. First, we propose a taxonomy of the manipulation primitives that are specifically involved in fine assembly. Such a taxonomy is crucial for designing a scalable robotic system (both hardware and software) given the complexity of real-world assembly tasks. Second, we present a hardware and software architecture where we have addressed, in an integrated way, a number of issues arising in fine assembly, such as workspace optimization, external wrench compensation, position-based force control, etc. Finally, we show the above taxonomy and architecture in action on a highly dexterous task - bimanual pin insertion - which is one of the key steps in our long term project, the autonomous assembly of a ready-to-assemble chair.

This paper presents a new Haptic Rate-Position controller, which allows manipulating a slave robot in a large workspace. Haptic information is displayed to the user so as to be informed when a change in the operation mode occurs. This controller allows performing tasks in a large remote workspace by using a haptic device with a reduced workspace such as Phantom. Experimental results have been carried out using a virtual slave robot, simulated with Open Dynamics Engine (ODE). A real IFMIF (International Fusion Materials Irradiation Facility) remote handling task has been simulated. Its goal is to carry out remote manipulation of test irradiated materials in a nuclear environment. The proposed algorithm has been compared with the classic Position controller in a Pick & Place manipulation and has shown much better levels of effectiveness.