Rapid In Situ Characterization of Soil Erodibility With a Field

Predicting the susceptibility of soil to wind erosion is difficult because it is a multivariate function of grain size, soil moisture, compaction, and biological growth. Erosive agents like plowing and grazing also differ in mechanism from entrainment by fluid shear; it is unclear if and how erosion thresholds for each process are related. Here we demonstrate the potential to rapidly assemble empirical maps of erodibility while also examining what controls it, using a novel “plowing” test of surface-soil shear resistance (𝜏r) performed by a semi-autonomous robot. Field work at White Sands National Monument, New Mexico, United States, examined gradients in erodibility at two scales: (i) soil moisture changes from dry dune crest to wet interdune (tens of meters) and (ii) downwind-increasing dune stabilization associated with growth of plants and salt and biological crusts (kilometers). We found that soil moisture changes of a few percent corresponded to a doubling of 𝜏r, a result confirmed by laboratory experiments, and that soil crusts conferred stability that was comparable to moisture effects. We then compared different mechanisms of mechanical perturbation in a controlled laboratory setting. A new “kick-out” test determines peak shear resistance of the surface soil as a proxy for yield strength. Kick-out resistance exhibited a relation with soil moisture that was distinct from the plowing test and that was correlated with the independently measured threshold-fluid stress for wind erosion. Results show that our new method maps soil erodibility in arid environments and provides an understanding of environmental controls on variations in soil erodibility. (For more information: Kod*lab)

Systematizing Gibsonian affordances in robotics: an empirical, generative approach derived

A Gibsonian theory of affordances commits to direct perception and the mutuality of the agent-environment system. We argue that there already exists a research program in robotics which incorporates Gibsonian affordances. Controllers under this research program use information perceived directly from the environment with little or no further processing, and implicitly respect the indivisibility of the agentenvironment system. Research investigating the relationships between environmental and robot properties can be used to design reactive controllers that provably allow robots to take advantage of these affordances. We lay out key features of our empirical, generative Gibsonian approach and both show how it illuminates existing practice and suggest that it could be adopted to facilitate the systematic development of autonomous robots. We limit the scope of projects discussed here to legged robot systems but expect that applications can be found in other fields of robotics research.

This paper was presented at the 2nd International Workshop on Computational Models of Affordances at ICRA 2019.

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Mitigating energy loss in a robot hopping on a physically

We work with geoscientists studying erosion and desertification to improve the spatial and temporal resolution of their data collection over long transects in difficult realworld environments such as deserts. The Minitaur robot, which can run quickly over uneven terrain and use a single leg to measure relevant ground properties such as stiffness, is an attractive scout robot candidate for inclusion in a heterogeneous team in collaboration with a heavily geared, sensor-laden RHex. However, Minitaur is challenged by long-distance locomotion on sand dunes. Previous simulation results suggested that the energetic cost of transport can be mitigated by programming a virtual damping force to slow the intrusion of a Minitaur foot into simulated granular media following a bulk-behavior force law. In this paper, we present a ground emulator that can be used to test such locomotion hypotheses with a physical single-legged hopper jumping on emulated ground programmed to exhibit any compliance and damping characteristics of interest. The new emulator allows us to corroborate the conclusions of our previous simulation with physical hopping experiments. Programming the substrate emulator to exhibit the mechanics of a simplified bulk-behavior model of granular media characterized by linear stiffness and quadratic damping, we achieve a consistent energy savings of 20% in comparison with a nominal controller, with savings of up to 50% under specific conditions.

For more information, see https://kodlab.seas.upenn.edu.

Task-Based Control and Design of a BLDC Actuator for Robotics

This paper proposes a new multi-input brushless DC motor current control policy aimed at robotics applications. The controller achieves empirical improvements in steady-state torque and power-production abilities relative to conventional controllers, while retaining similarly good torque-tracking and stability characteristics. Simulations show that non-conventional motor design optimizations whose feasibility is established by scaling model extrapolations from existing motor catalogues can vastly amplify the effectiveness of this new control-strategy.

Iterated Belief Revision Under Resource Constraints: Logic as Geometry

We propose a variant of iterated belief revision designed for settings with limited computational resources, such as mobile autonomous robots.

The proposed memory architecture—called the universal memory architecture (UMA)—maintains an epistemic state in the form of a system of default rules similar to those studied by Pearl and by Goldszmidt and Pearl (systems Z and Z+). A duality between the category of UMA representations and the category of the corresponding model spaces, extending the Sageev-Roller duality between discrete poc sets and discrete median algebras provides a two-way dictionary from inference to geometry, leading to immense savings in computation, at a cost in the quality of representation that can be quantified in terms of topological invariants. Moreover, the same framework naturally enables comparisons between different model spaces, making it possible to analyze the deficiencies of one model space in comparison to others.

This paper develops the formalism underlying UMA, analyzes the complexity of maintenance and inference operations in UMA, and presents some learning guarantees for different UMA-based learners. Finally, we present simulation results to illustrate the viability of the approach, and close with a discussion of the strengths, weaknesses, and potential development of UMA-based learners.

Reactive Navigation in Partially Known Non-Convex Environments

This paper presents a provably correct method for robot navigation in 2D environments cluttered with familiar but unexpected non-convex, star-shaped obstacles as well as completely unknown, convex obstacles. We presuppose a limited range onboard sensor, capable of recognizing, localizing and (leveraging ideas from constructive solid geometry) generating online from its catalogue of the familiar, non-convex shapes an implicit representation of each one. These representations underlie an online change of coordinates to a completely convex model planning space wherein a previously developed online construction yields a provably correct reactive controller that is pulled back to the physically sensed representation to generate the actual robot commands. We extend the construction to differential drive robots, and suggest the empirical utility of the proposed control architecture using both formal proofs and numerical simulations.

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Actuator Transparency and the Energetic Cost of Proprioception

In the field of haptics, conditions for mechanical “transparency”[1] entail such qualities as “solid virtual objects must feel stiff” and “free space must feel free”[2], suggesting that a suitable actuator is able both to do work and readily have work done on it. In this context, seeking actuator transparency has come to mean a preference for minimal dynamics [3] or no impedance [4]. While such general notions seem satisfactory for a haptic interface, actuators with good mechanical transparency are now being used in high-performance robots [5, 6] where once again they must be able to do work, but are now also expected to perceive their environment by processing signals related to contact forces in the leg or manipulator when an explicit force sensor is not present. As robotics researchers develop models [7] suitable for programming behaviors that require systematic making and breaking of contact within the environments on which they perform work, actuators must be capable of: (a) generating the high forces at speed needed to accelerate the body during locomotion [5]; (b) robustness to high forces and impacts during locomotion [8]; (c) perceiving high force events quickly, such as touchdown in stance [9]; (d) perceiving contact quickly without exerting significant force on the object, such as in gentle manipulation [10]; and (e) reacting quickly during time-sensitive behaviors [11].

This work aims to describe a quantitative assay of transparency that might, for example, predict the advantage in proprioceptive tasks of an electromagnetic directdrive (DD) motor (i.e., one without gearbox), relative to actuation schemes consisting of both a motor and a geared reduction. Specifically, we explore the prospects for characterizing transparency as revealed by comparing the energetic cost of “feeling” the environment. Our sample proprioceptive task is instantiated by a simple torque estimator in Sec. 2. This scheme is then instrumented in simple contact detection experiments paired with a model to empirically explore the relationships between collision energy and detection time delay in Sec. 3. The actuators are then tested with a feel-cage task to illustrate the advantage of good transparency in Sec. 4.

“For more information: Kod*lab (link to kodlab.seas.upenn.edu)

Reactive Velocity Control Reduces Energetic Cost of Jumping with a

Robots capable of dynamic locomotion behaviors and high-bandwidth sensing with their limbs have a high cost of transport, especially when locomoting over highly dissipative substrates such as sand. We formulate the problem of reducing the energetic cost of locomotion by a Minitaur robot on sand, reacting to robot state variables in the inertial world frame without modeling the ground online. Using a bulk-behavior model of high-velocity intrusions into dry granular media, we simulated single jumps by a one-legged hopper using a Raibert-style compression-extension virtual leg spring. We compose this controller with a controller that added damping to the leg spring in proportion to the intrusion velocity of the robot’s foot into the simulated sand while the robot is pushing off in the second half of stance. This has the effect of both reducing the torque exerted by the motors because the added virtual “active damping” force acts in opposition to the virtual leg spring force, and reducing the transfer of energy from the robot to the sand by slowing the intrusion velocity of the foot. Varying the simulated robot’s initial conditions and the simulated ground parameters, we gained a consistent 20% energy savings by adding active damping with no cost in apex height.

For more information, see the Kod*lab website: kodlab.seas.upenn.edu

Sensor-Based Reactive Execution of Symbolic Rearrangement Plans by a Legged

We demonstrate the physical rearrangement of wheeled stools in a moderately cluttered indoor environment by a quadrupedal robot that autonomously achieves a user’s desired configuration. The robot’s behaviors are planned and executed by a three layer hierarchical architecture consisting of: an offline symbolic task and motion planner; a reactive layer that tracks the reference output of the deliberative layer and avoids unanticipated obstacles sensed online; and a gait layer that realizes the abstract unicycle commands from the reactive module through appropriately coordinated joint level torque feedback loops. This work also extends prior formal results about the reactive layer to a broad class of nonconvex obstacles. Our design is verified both by formal proofs as well as empirical demonstration of various assembly tasks.

For more information: Kod*lab