Multi-Agent Path Finding (MAPF) is an important optimization problem underlying the deployment of robots in automated warehouses and factories. Despite the large body of work on this topic, most approaches make heavy simplifications, both on the environment and the agents, which make the resulting algorithms impractical for real-life scenarios. In this paper, we consider a realistic problem of online order delivery in a warehouse, where a fleet of robots bring the products belonging to each order from shelves to workstations. This creates a stream of inter-dependent pickup and delivery tasks and the associated MAPF problem consists of computing realistic collision-free robot trajectories fulfilling these tasks. To solve this MAPF problem, we propose an extension of the standard Prioritized Planning algorithm to deal with the inter-dependent tasks (Interleaved Prioritized Planning) and a novel Via-Point Star (VP*) algorithm to compute an optimal dynamics-compliant robot trajectory to visit a sequence of goal locations while avoiding moving obstacles. We prove the completeness of our approach and evaluate it in simulation as well as in a real warehouse.

Manipulation involves a broad spectrum of skills, e.g., polishing, peeling, flipping, screwing, etc., requiring complex and delicate control over both force and position. This paper aims at designing an optimal haptic interface for providing a robot with direct demonstrations of human's innate intelligence in performing a wide range of force-based bimanual manipulation tasks. Based on the proprioceptive actuation mechanism, kinodynamic design parameters of the (dual) 7-DOF haptic arm are optimized so as to maximize the force transparency perceived by the operator over the full real-scale workspace of human arm while also ensuring other important constraints including robot-to-operator collision and singularity avoidance, payload, controlled stiffness, etc. 2.65 kg of average reflective mass and 1500 N/m of controlled stiffness is achieved over the entire workspace. We show the efficacy of our haptic interface by demonstrating various force-based manipulation tasks with a light-weight anthropomorphic bimanual manipulator, LIMS2-AMBIDEX [1].

Designing an actuator system for highly dynamic legged robots has been one of the grand challenges in robotics research. Conventional actuators for manufacturing applications have difficulty satisfying design requirements for high-speed locomotion, such as the need for high torque density and the ability to manage dynamic physical interactions. To address this challenge, this paper suggests a proprioceptive actuation paradigm that enables highly dynamic performance in legged machines. Proprioceptive actuation uses collocated force control at the joints to effectively control contact interactions at the feet under dynamic conditions. Modal analysis of a reduced leg model and dimensional analysis of DC motors address the main principles for implementation of this paradigm. In the realm of legged machines, this paradigm provides a unique combination of high torque density, high-bandwidth force control, and the ability to mitigate impacts through backdrivability. We introduce a new metric named the “impact mitigation factor” (IMF) to quantify backdrivability at impact, which enables design comparison across a wide class of robots. The MIT Cheetah leg is presented, and is shown to have an IMF that is comparable to other quadrupeds with series springs to handle impact. The design enables the Cheetah to control contact forces during dynamic bounding, with contact times down to 85 ms and peak forces over 450 N. The unique capabilities of the MIT Cheetah, achieving impact-robust force-controlled operation in high-speed three-dimensional running and jumping, suggest wider implementation of this holistic actuation approach.

This paper presents the ongoing work towards enabling humanoid robots to achieve dynamic behaviors comparable to humans. By using the concept of Divergent Component of Motion (DCM) first in introduced in [1], the present paper permits operator and robot balance synchronization using the mutual dynamics of the Center of Mass (CoM) and Center of Pressure (CoP). The Linear Inverted Pendulum Model (LIPM) with a reaction mass [2] is utilized to capture the humanoid behavior which interacts with the human operator under the Equilibrium Point (EP) [3] control assumption. Remarkable similarities between the physical system behavior and simulated results suggest the feasibility of the strategy. Experiments conducted with the MIT HERMES humanoid robot further show the performance of the proposed method.

This paper introduces the phenomenon of directional efficiency, a property of spur gear transmissions in which the coefficient of torque dependent efficiency is function of the power flow direction; the efficiency in the step-up direction differs from that of step-down. Understanding the directional efficiency is important in robot design where the mechanical impedance of the transmission significantly affects the dynamics of physical interaction with the environment. This paper is the first to address `backdrivability' by investigating directional efficiency on a high gear ratio robotic actuator, industrial servo gearboxes and over a family of gears. The kinematic model shows that a single pair of gears operates at higher efficiency in step-down than in step-up and the effect is confirmed in experiment, albeit with discrepancy in the magnitude of difference. Experimental results show that step-down and step-up directional efficiency are 98% and 96% respectively for a PIC DESIGNS 6:1 gearbox over the range of tested velocities. Additionally the overall energy efficiencies of the Dynamixel robotic servo motor with a gear ratio of 248:1 are approximately 75% and 70% for step-down and step-up respectively over a range of tested velocities. For the Dynamixel servo actuator, modeling with directional efficiency coefficients reduces error in predicted output force to 2-4% compared to a 4-8% with a single efficiency coefficient and 30% for models without friction.

This paper introduces a novel Balance Feedback Interface (BFI) that addresses the problem of bilateral feedback for teleoperation of humanoid robots. With this new device we expect to enhance robot's high force manipulation performance to a level comparable to humans by dynamically synchronizing master and slave. Through this Human-Machine Interface (HMI) we aim to achieve two goals: (i) have the human pilot learn from the robot's dynamic behavior; and (ii) have the humanoid robotic platform learn from human's motor skills. The eventual goal is to fuse the teleoperator's commands and an autonomous controller for optimal performance. Initial results evaluate the stability of the robot while being teleoperated by a human pilot for a simple upright balancing task. During the experiment the user has no visual information about the robot's state and is expected to compensate for unpredicted instabilities. The bilateral feedback system shows robustness to inertial variations and to external disturbances with the proposed human-in-the-loop control strategy offering valuable insight for future work.

In this paper, we introduce the design principles for highly efficient legged robots and the implementation of the principles on the MIT Cheetah robot. Three major energy loss modes during locomotion are heat losses through the actuators, losses through the transmission, and the interaction losses that includes all losses of the system interacting with the environment. We propose four design principles that minimize these losses: employment of high torque density motors, low impedance transmission, energy regenerative electronics and a design architecture that minimizes the leg inertia. We present the design features of the MIT cheetah robot as an embodiment of these principles. The resulting cost of transport (COT) is 0.51 during 2.3 m/s running, which rivals running animals in the same scale.

High speed legged locomotion involves high acceleration and extensive loadings of the leg, which impose critical challenges in actuator design. We introduce actuator dimensional analysis for maximizing torque density and transmission `transparency'. A front leg prototype developed based on insight from the analysis is evaluated for direct proprioceptive force control without force sensors. The vertical stiffness controlled leg was tested on a material testing device to calibrate the mechanical impedance of the leg. By compensating transmission impedance from commanded torque, the leg was able to estimate impact force. For the impact test, the mean absolute error as a ratio of full scale sensor force is 0.041 in the 3406 N/m stiffness experiment and is 0.049 in the 5038 N/m experiment. The results indicate that prescribed force profile control is possible during high speed locomotion.