RSS 2021 Papers

Today; even the most compute-and-power constrained robots can measure complex; high data-rate video and LIDAR sensory streams. Often; such robots; ranging from low-power drones to space and subterranean rovers; need to transmit high-bitrate sensory data to a remote compute server if they are uncertain or cannot scalably run complex perception or mapping tasks locally. However; today's representations for sensory data are mostly designed for human; not robotic; perception and thus often waste precious compute or wireless network resources to transmit unimportant parts of a scene that are unnecessary for a high-level robotic task. This paper presents an algorithm to learn task-relevant representations of sensory data that are co-designed with a pre-trained robotic perception model's ultimate objective. Our algorithm aggressively compresses robotic sensory data by up to 11x more than competing methods. Further; it achieves high accuracy and robust generalization on diverse tasks including Mars terrain classification with low-power deep learning accelerators; neural motion planning; and environmental timeseries classification.

Accurate and precise terrain estimation is a difficult problem for robot locomotion in real-world environments. Thus; it is useful to have systems that do not depend on accurate estimation to the point of fragility. In this paper; we explore the limits of such an approach by investigating the problem of traversing stair-like terrain without any external perception or terrain models on a bipedal robot. For such blind bipedal platforms; the problem appears difficult (even for humans) due to the surprise elevation changes. Our main contribution is to show that sim-to-real reinforcement learning (RL) can achieve robust locomotion over stair-like terrain on the bipedal robot Cassie using only proprioceptive feedback. Importantly; this only requires modifying an existing flat-terrain training RL framework to include stair-like terrain randomization; without any changes in reward function. To our knowledge; this is the first controller for a bipedal; human-scale robot capable of reliably traversing a variety of real-world stairs and other stair-like disturbances using only proprioception.

Correspondence identification is essential for multi-robot collaborative perception; which aims to identify the same objects in order to ensure consistent references of the objects by a group of robots/agents in their own fields of view. Although recent deep learning methods have shown encouraging performance on correspondence identification; they suffer from two shortcomings; including the inability to address non-covisibility in collaborative perception that is caused by occlusion and limited fields of view of the agents; and the inability to quantify and reduce uncertainty to improve correspondence identification. To address both issues; we propose a novel uncertainty-aware deep graph matching method for correspondence identification in collaborative perception. Our method formulates correspondence identification as a deep graph matching problem; which identifies correspondences based upon graph representations that are constructed from robot observations. We propose new deep graph matching networks in the Bayesian framework to explicitly quantify uncertainty in identified correspondences. In addition; we design novel loss functions in order to explicitly reduce correspondence uncertainty and perceptual non-covisibility during learning. We evaluate our method in the robotics applications of collaborative assembly and multi-robot coordination using high-fidelity simulations and physical robots. Experiments have validated that; by addressing uncertainty and non-covisibility; our proposed approach achieves the state-of-the-art performance of correspondence identification.

The current dominant paradigm for robotic manipulation involves two separate stages: manipulator design and control. Because the robot's morphology and how it can be controlled are intimately linked; joint optimization of design and control can significantly improve performance. Existing methods for co-optimization are limited and fail to explore a rich space of designs. The primary reason is the trade-off between the complexity of designs that is necessary for contact-rich tasks against the practical constraints of manufacturing; optimization; contact handling; etc. We overcome several of these challenges by building an end-to-end differentiable framework for contact-aware robot design. The two key components of this framework are: a novel deformation-based parameterization that allows for the design of articulated rigid robots with arbitrary; complex geometry; and a differentiable rigid body simulator that can handle contact-rich scenarios and computes analytical gradients for a full spectrum of kinematic and dynamic parameters. On multiple manipulation tasks; our framework outperforms existing methods that either only optimize for control or for design using alternate representations or co-optimize using gradient-free methods.

In order to provide adaptive and user-friendly solutions to robotic manipulation; it is important that the agent can learn to accomplish tasks even if they are only provided with very sparse instruction signals. To address the issues reinforcement learning algorithms face when task rewards are sparse; this paper proposes an intrinsic motivation approach that can be easily integrated into any standard reinforcement learning algorithm and can allow robotic manipulators to learn useful manipulation skills with only sparse extrinsic rewards. Through integrating and balancing empowerment and curiosity; this approach shows superior performance compared to other state-of-the-art intrinsic exploration approaches during extensive empirical testing. Qualitative analysis also shows that when combined with diversity-driven intrinsic motivations; this approach can help manipulators learn a set of diverse skills which could potentially be applied to other more complicated manipulation tasks and accelerate their learning process.

Real-time adaptation is imperative to the control of robots operating in complex; dynamic environments. Adaptive control laws can endow even nonlinear systems with good trajectory tracking performance; provided that any uncertain dynamics terms are linearly parameterizable with known nonlinear features. However; it is often difficult to specify such features a priori; such as for aerodynamic disturbances on rotorcraft or interaction forces between a manipulator arm and various objects. In this paper; we turn to data-driven modeling with neural networks to learn; offline from past data; an adaptive controller with an internal parametric model of these nonlinear features. Our key insight is that we can better prepare the controller for deployment with control-oriented meta-learning of features in closed-loop simulation; rather than regression-oriented meta-learning of features to fit input-output data. Specifically; we meta-learn the adaptive controller with closed-loop tracking simulation as the base-learner and the average tracking error as the meta-objective. With a nonlinear planar rotorcraft subject to wind; we demonstrate that our adaptive controller outperforms other controllers trained with regression-oriented meta-learning when deployed in closed-loop for trajectory tracking control.

To safely navigate unknown environments; robots must accurately perceive dynamic obstacles. Instead of directly measuring the scene depth with a LiDAR sensor; we explore the use of a much cheaper and higher resolution sensor: programmable light curtains. Light curtains are controllable depth sensors that sense only along a surface that a user selects. We use light curtains to estimate the safety envelope of a scene: a hypothetical surface that separates the robot from all obstacles. We show that generating light curtains that sense random locations (from a particular distribution) can quickly discover the safety envelope for scenes with unknown objects. Importantly; we produce theoretical safety guarantees on the probability of detecting an obstacle using random curtains. We combine random curtains with a machine learning based model that forecasts and tracks the motion of the safety envelope efficiently. Our method accurately estimates safety envelopes while providing probabilistic safety guarantees that can be used to certify the efficacy of a robot perception system to detect and avoid dynamic obstacles. We evaluate our approach in a simulated urban driving environment and a real-world environment with moving pedestrians using a light curtain device and show that we can estimate safety envelopes efficiently and effectively.

Long horizon sequential manipulation tasks are effectively addressed hierarchically: at a high level of abstraction the planner searches over abstract action sequences; and when a plan is found; lower level motion plans are generated. Such a strategy hinges on the ability to reliably predict that a feasible low level plan will be found which satisfies the abstract plan. However; computing Abstract Plan Feasibility (APF) is difficult because the outcome of a plan depends on complex real-world phenomena that are computationally costly to model; such as noise in estimation and plan execution. In this work; we present an active learning approach to efficiently acquire an APF predictor through curious exploration on a robot. The robot identifies plans whose outcomes would be informative about APF; executes those plans; and learns from their subsequent successes or failures. We evaluate our strategy in simulation and on a real Franka Emika Panda robot with integrated perception; experimentation; planning; and execution. In a stacking domain where objects have non-uniform mass distributions; we show that our system permits real-robot learning of an APF model in four hundred self-supervised interactions; and that our learned model can be used effectively in different downstream tasks (e.g.; constructing the tallest tower or tower with the longest overhang).

This paper presents Particle-based Object Manipulation (PROMPT); a new approach to robot manipulation of novel objects ab initio; without prior object models or pre-training on a large object data set. The key element of PROMPT is a particle-based object representation; in which each particle represents a point in the object; the local geometric; physical; and other features of the point; and also its relation with other particles. Like the model-based analytic approaches to manipulation; the particle representation enables the robot to reason about the object's geometry and dynamics in order to choose suitable manipulation actions. Like the data-driven approaches; the particle representation is inferred online in real-time from visual sensor input; specifically; multi-view RGB images. The particle representation thus connects visual perception with robot control. PROMPT combines the benefits of both model-based reasoning and data-driven learning. We show empirically that PROMPT successfully handles a variety of everyday objects; some of which are transparent. It handles various manipulation tasks; including grasping; pushing; etc;. Our experiments also show that PROMPT outperforms a state-of-the-art data-driven grasping method on the daily objects; even though it does not use any offline training data.

The growth of scale and complexity of interactions between humans and robots highlights the need for new computational methods to automatically evaluate novel algorithms and applications. Exploring diverse scenarios of humans and robots interacting in simulation can improve understanding of the robotic system and avoid potentially costly failures in real-world settings. We formulate this problem as a quality diversity (QD) problem; where the goal is to discover diverse failure scenarios by simultaneously exploring both environments and human actions. We focus on the shared autonomy domain; where the robot attempts to infer the goal of a human operator; and adopt the QD algorithm MAP-Elites to generate scenarios for two published algorithms in this domain: shared autonomy via hindsight optimization and linear policy blending. Some of the generated scenarios confirm previous theoretical findings; while others are surprising and bring about a new understanding of state-of-the-art implementations. Our experiments show that MAP-Elites outperforms Monte-Carlo simulation and optimization based methods in effectively searching the scenario space; highlighting its promise for automatic evaluation of algorithms in human-robot interaction.

Traditional parallel-jaw grippers are insufficient for delicate object manipulation due to their stiffness and lack of dexterity. Other dexterous robotic hands often have bulky fingers; rely on complex time-varying cable drives; or are prohibitively expensive. In this paper; we introduce a novel low-cost compliant gripper with two centimeter-scaled 3-DOF delta robots using off-the-shelf linear actuators and 3D-printed soft materials. To model the kinematics of delta robots with soft compliant links; which diverge from typical rigid links; we train neural networks using a perception system. Furthermore; we analyze the delta robot's force profile by varying the starting position in its workspace and measuring the resulting force from a push action. Finally; we demonstrate the compliance and dexterity of our gripper through six dexterous manipulation tasks involving small and delicate objects. Thus; we present the groundwork for creating modular multi-fingered hands that can execute precise and low-inertia manipulations.