Picking diverse objects in the real world is a fundamental robotics skill. However, many objects in such settings are bulky, heavy, or irregularly shaped, making them ungraspable by conventional end effectors like suction grippers and parallel jaw grippers (PJGs). In this paper, we expand the range of pickable items without hardware modifications using bimanual nonprehensile manipulation. We focus on a grocery shopping scenario, where a bimanual mobile manipulator equipped with a suction gripper and a PJG is tasked with re-trieving ungraspable items from tightly packed grocery shelves. From visual observations, our method first identifies optimal grasp points based on force closure and friction constraints. If the grasp points are occluded, a series of nonprehensile nudging motions are performed to clear the obstruction. A bimanual grasp utilizing contacts on the side of the end effectors is then executed to grasp the target item. In our replica grocery store, we achieved a 90% success rate over 102 trials in uncluttered scenes, and a 67 % success rate over 45 trials in cluttered scenes. We also deployed our system to a real-world grocery store and successfully picked previously unseen items. Our results highlight the potential of bimanual nonprehensile manipulation for in-the-wild robotic picking tasks. A video summarizing this work can be found at youtu. be/gOhOrDuK8jM.

Extrinsic manipulation, the use of environment contacts to achieve manipulation objectives, enables strategies that are otherwise impossible with a parallel jaw gripper. However, orchestrating a long-horizon sequence of contact interactions between the robot, object, and environment is notoriously challenging due to the scene diversity, large action space, and difficult contact dynamics. We observe that most extrinsic manipulation are combinations of short-horizon primitives, each of which depend strongly on initializing from a desirable contact configuration to succeed. Therefore, we propose to generalize one extrinsic manipulation trajectory to diverse objects and environments by retargeting contact requirements. We prepare a single library of robust short-horizon, goal-conditioned primitive policies, and design a framework to compose state constraints stemming from contacts specifications of each primitive. Given a test scene and a single demo prescribing the primitive sequence, our method enforces the state constraints on the test scene and find intermediate goal states using inverse kinematics. The goals are then tracked by the primitive policies. Using a 7+1 DoF robotic arm-gripper system, we achieved an overall success rate of 80.5% on hardware over 4 long-horizon extrinsic manipulation tasks, each with up to 4 primitives. Our experiments cover 10 objects and 6 environment configurations. We further show empirically that our method admits a wide range of demonstrations, and that contact retargeting is indeed the key to successfully combining primitives for long-horizon extrinsic manipulation. Code and additional details are available at stanford-tml.github. io/extrinsic-manipulation.

A summer 2021 European airborne field campaign-the Land surface Interactions with the Atmosphere over the Iberian Semi-arid Environment (LIAISE) campaign-presented an opportunity to explore passive soil moisture sensing with footprints as small as 100x200m, contributing a key measurement to LIAISE and providing a rare opportunity to gain detailed insight into the water/energy/carbon exchanges at such plot-scale resolution over a$17 \mathrm{x}5$km area. NASA Goddard's Scanning L-band Active Passive (SLAP) sensor-an airborne simulator of the Soil Moisture Active Passive (SMAP) satellite-made nine soil moisture flights near Lleida, Spain during 15–29 July. We present soil moisture imagery and histograms spanning irrigated and non-irrigated land and their response to a precipitation event followed by a drydown.

Real-world systems are often comprised of interconnected units, and can be represented as networks, with nodes and edges. In a social system, for instance, individuals may have social ties and financial relationships. In these settings, when a node (the unit analysis) is exposed to a treatment, its effects may spill over to connected units; then estimating both the direct effect of the treatment and its spillover effects presents several challenges. First, assumptions about the mechanism through which spillover effects occur along the observed network are required. Second, in observational studies, where the treatment assignment has not been randomized, confounding and homophily are further potential threats to the identification and to the estimation of causal effects, on networks. Here, we make two structural assumptions: (i) neighborhood interference, which assumes interference operates only through a function of the immediate neighbors' treatments, and (ii) unconfoundedness of the individual and neighborhood treatment, which rules out the presence of unmeasured confounding variables, including those driving homophily. Under these assumptions we develop a new covariate-adjustment estimator for direct treatment and spillover effects in observational studies on networks. We proposed an estimation strategy based on a generalized propensity score that balances individual and neighborhood covariates across units under different levels of individual treatment and of exposure to neighbors' treatment. Adjustment for propensity score is performed using a penalized spline regression. Our inference strategy capitalizes on a three-step Bayesian procedure, which allows to take account for the uncertainty in the propensity score estimation, and avoids model feedback. The correlation among connected units is taken into account using a community detection algorithm, and incorporating random effects in the outcome model. All these sources of variability, including variability of treatment assignment, are accounted for in the posterior distribution of the finite-sample causal estimands we target. We design a simulation study to assess the performance of the proposed estimator on different network topologies, both on synthetic networks and on real friendship network from the Add-Health study.

We introduce R3T, a reachability-based variant of the rapidly-exploring random tree (RRT) algorithm that is suitable for (optimal) kinodynamic planning in nonlinear and hybrid systems. We developed tools to approximate reachable sets using polytopes and perform sampling-based planning with them. This method has a unique advantage in hybrid systems: different dynamic modes in the reachable set can be explicitly represented using multiple polytopes. We prove that under mild assumptions, R3T is probabilistically complete in kinodynamic systems, and asymptotically optimal through rewiring. Moreover, R3T provides a formal verification method for reachability analysis of nonlinear systems. The advantages of R3T are demonstrated with case studies on nonlinear, hybrid, and contact-rich robotic systems.

Lower-limb exoskeletons are of great interest in the robotics community because of their various applications in enhancement and rehabilitation. In this paper we present an autonomous exoskeleton platform for ankle plantarflexion assistance. The untethered exoskeleton has a high efficiency transmission system with reduction ratio of 27.4:1. This allows relocating the actuator to the wearer's hip, which reduces device inertia. A feed-forward controller based on field oriented control was implemented to control the brushless DC motor on the exoskeleton. Through various performance tests, the exoskeleton was shown to provide a torque control bandwidth of 17.5Hz and can effectively track biological torque profiles. The augmentation factor (AF) of the exoskeleton is 64.7W, implying potential to reduce walking metabolic cost. This exoskeleton establishes an autonomous platform for experiments involving ankle assistance.

In most real-world systems units are interconnected and can be represented as networks consisting of nodes and edges. For instance, in social systems individuals can have social ties, family or financial relationships. In settings where some units are exposed to a treatment and its effects spills over connected units, estimating both the direct effect of the treatment and spillover effects presents several challenges. First, assumptions on the way and the extent to which spillover effects occur along the observed network are required. Second, in observational studies, where the treatment assignment is not under the control of the investigator, confounding and homophily are potential threats to the identification and estimation of causal effects on networks. Here, we make two structural assumptions: i) neighborhood interference, which assumes interference to operate only through a function of the the immediate neighbors' treatments, ii) unconfoundedness of the individual and neighborhood treatment, which rules out the presence of unmeasured confounding variables, including those driving homophily. Under these assumptions we develop a new covariate-adjustment estimator for treatment and spillover effects in observational studies on networks. Estimation is based on a generalized propensity score that balances individual and neighborhood covariates across units under different levels of individual treatment and of exposure to neighbors' treatment. Adjustment for propensity score is performed using a penalized spline regression. Inference capitalizes on a three-step Bayesian procedure which allows taking into account the uncertainty in the propensity score estimation and avoiding model feedback. Finally, correlation of interacting units is taken into account using a community detection algorithm and incorporating random effects in the outcome model. All these sources of variability, including variability of treatment assignment, are accounted for in the posterior distribution of finite-sample causal estimands.

Permanently frozen and seasonally frozen soils occur over a large portion of the Earth's land surface. Changes in the freeze/thaw state of the land surface reflects major changes in thermal and hydraulic properties as well as acting as a “switch” for many ecological processes. In short, soil freeze/thaw state is a fundamental land surface variable in the water and energy cycles, and it connects to the carbon cycle. Surface freeze/thaw state is observable by passive and active microwave sensors. For example, NASA's Soil Moisture Active Passive (SMAP) mission includes a freeze/thaw data product. Such satellite sensing offers routine all-season and all-weather global observations of soil freeze/thaw state with the application of suitable algorithms. We describe early finding from the SLAPex Freeze/Thaw campaign, believed to be the first airborne campaign of its type, focusing on soil freeze/thaw.

Scanning L-band Active/Passive (SLAP) is an airborne remote sensing instrument developed at NASA Goddard Space Flight Center specifically as an airborne simulator of the Soil Moisture Active/Passive (SMAP) satellite instrument suite, for remote sensing of soil moisture, freeze-thaw state, ocean salinity, sea ice, and other physical phenomena that display characteristics at microwave L-band. This paper will present example observations from a recent soil freeze-thaw campaign in Winnipeg, Manitoba, Canada, as well as detail enhancements made to the SLAP RFI processor beyond the capabilities of the SMAP RFI processor.

The analysis of the conceptual spring mass model for running reveals swing-leg placement policies that generate very robust locomotion in unobserved terrain with large changes in ground height. However, while this theoretical result suggests a potential for large improvements on the robustness of running machines, it has so far not been demonstrated on a physical robot. Here we address this implementation and verification step for a human-sized bipedal robot platform confined to a boom. We detail challenges and solutions for the implementation of the control approach and show that it leads to very robust running (ground changes ±20% of leg length) over unobserved ground in a high fidelity simulation of the robot platform. We also present initial tests on the actual robot hardware, which indicate the feasibility of the approach. If it can be generalized to 3D running, it could trigger running machines with largely improved robustness.

In this paper we propose the fastest converging control policy (also known as deadbeat control) for walking with the bipedal spring-mass model, which serves as an abstraction of a robot on compliant legs. To fully leverage the passive dynamics of the system, the touchdown angle of the swing-leg is assigned as the only control input of the system. We show that two steps (or one stride) are necessary and sufficient to converge to target walking gaits. We first analyze the dynamics of the system to identify the limit cycles as well as the limitations of the control authority within the definition of walking. Then, we present the two-step deadbeat control policy that guarantees stability with the fastest possible convergence rate for the system. For each equilibrium gait, the basin of attraction in which this two-step control exists is a measure of the robustness of the system. The simulation results show that human-like walking gaits (double hump ground reaction force profile) have relatively large basins of attraction. Finally, we extend the policy to various energy levels to accommodate walking on uneven ground that has height changes. We show in simulation that the system indeed rejects various disturbances and converges to the desired equilibrium gait in two steps.

Automatic assessment reduces the need for individual feedback in massive courses, but often focuses only on scoring solutions, rather than assessing whether students correctly understand problems. We present an enriched approach to automatic assessment that explicitly assists students in understanding the detailed specification of technical problems that they are asked to solve, in addition to evaluating their solutions. Students are given a suite of solution test cases, but they must first unlock each test case by validating its behavior before they are allowed to apply it to their proposed solution. When provided with this automated feedback early in the problem-solving process, students ask fewer clarificatory questions and express less confusion about assessments. As a result, instructors spend less time explaining problems to students. In a 1300-person university course, we observed that the vast majority of students chose to validate their understanding of test cases before attempting to solve problems. These students reported that the validation process improved their understanding.

We propose a method for combining two sources of astronomical data, spectroscopy and photometry, that carry information about sources of light (e.g., stars, galaxies, and quasars) at extremely different spectral resolutions. Our model treats the spectral energy distribution (SED) of the radiation from a source as a latent variable that jointly explains both photometric and spectroscopic observations. We place a flexible, nonparametric prior over the SED of a light source that admits a physically interpretable decomposition, and allows us to tractably perform inference. We use our model to predict the distribution of the redshift of a quasar from five-band (low spectral resolution) photometric data, the so called ``photo-z'' problem. Our method shows that tools from machine learning and Bayesian statistics allow us to leverage multiple resolutions of information to make accurate predictions with well-characterized uncertainties.

Control design of running robots is often based on mapping the behavior of lower order models onto the robotic systems, and the robustness of running is largely determined by the robustness of these underlying models. However, existing implementations do not take full advantage of the stability that the low order models can provide. In particular, analysis of the theoretical spring mass model suggests leg placement policies that generate near deadbeat rejection of large, unobserved changes in ground height. Here we show in simulation that this blind robustness to rough terrain can be carried over to bipedal robots. We design a control that stably embeds the spring mass model's behavior in a planar robot model and show that resulting system rejects ground disturbances of up to 25% leg length, adapts to persistent ground slopes, and tolerates sensor noise, signal delays, and modeling errors. The results indicate that transferring control derived within the spring mass model is an effective technique for realizing highly robust running in robotic systems.

Over the past three decades, the spring-mass model has developed into the basic behavior model to study running in animals and robots. In the planar version, this model has helped to reveal and understand the passive stabilization of running in the horizontal and sagittal planes, and to derive from this knowledge control strategies for running robots. However, only few attempts have been made to transfer the knowledge to 3-D locomotion. Here, we show that the 3-D spring-mass model reveals a deadbeat control that does not require feedback about the actual ground level to produce highly robust running and steering in uncertain environments. The control naturally extends the time-based control derived for the planar version of this model and allows it to navigate rough terrain, while stabilizing running and steering. Using this control strategy, we demonstrate in simulation that a human-like system running at 5 ms-1tolerates frequent ground disturbances up to 30% of the leg length. Moreover, we find that the control outperforms a classical leg-placement strategy in terms of turning rate and disturbance rejection if the relative errors in system energy and the other model parameters stay small ( 10%). Our results suggest that the time-based control can be a powerful alternative for leg-placement strategies in highly maneuverable running robots.