Collision-inclusive Manipulation Planning for Occluded Object Grasping via Compliant Robot Motions

Traditional robotic manipulation mostly focuses on collision-free tasks. In practice, however, many manipulation tasks (e.g., occluded object grasping) require the robot to intentionally collide with the environment to reach a desired task configuration. By enabling compliant robot motions, collisions between the robot and the environment are allowed and can thus be exploited, but more physical uncertainties are introduced. To address collision-rich problems such as occluded object grasping while handling the involved uncertainties, we propose a collision-inclusive planning framework that can transition the robot to a desired task configuration via roughly modeled collisions absorbed by Cartesian impedance control. By strategically exploiting the environmental constraints and exploring inside a manipulation funnel formed by task repetitions, our framework can effectively reduce physical and perception uncertainties. With real-world evaluations on both single-arm and dual-arm setups, we show that our framework is able to efficiently address various realistic occluded grasping problems where a feasible grasp does not initially exist.

Caging in Time: A Framework for Robust Object Manipulation under Uncertainties and Limited Robot Perception

Real-world object manipulation has been commonly challenged by physical uncertainties and perception limitations. Being an effective strategy, while caging configuration-based manipulation frameworks have successfully provided robust solutions, they are not broadly applicable due to their strict requirements on the availability of multiple robots, widely distributed contacts, or specific geometries of the robots or the objects. To this end, this work proposes a novel concept, termed Caging in Time, to allow caging configurations to be formed even if there is just one robot engaged in a task. This novel concept can be explained by an insight that even if a caging configuration is needed to constrain the motion of an object, only a small portion of the cage is actively manipulating at a time. As such, we can switch the configuration of the robot strategically so that by collapsing its configuration in time, we will see a cage formed and its necessary portion active whenever needed. We instantiate our Caging in Time theory on challenging quasistatic and dynamic manipulation tasks, showing that Caging in Time can be achieved in general state spaces including geometry-based and energy-based spaces. With extensive experiments, we show robust and accurate manipulation, in an open-loop manner, without requiring detailed knowledge of the object geometry or physical properties, nor realtime accurate feedback on the manipulation states. In addition to being an effective and robust open-loop manipulation solution, the proposed theory can be a supplementary strategy to other manipulation systems affected by uncertain or limited robot perception.

UNO Push: Unified Nonprehensile Object Pushing via Non-Parametric Estimation and Model Predictive Control

Nonprehensile manipulation through precise pushing is an essential skill that has been commonly challenged by perception and physical uncertainties, such as those associated with contacts, object geometries, and physical properties. For this, we propose a unified framework that jointly addresses system modeling, action generation, and control. While most existing approaches either heavily rely on a priori system information for analytic modeling, or leverage a large dataset to learn dynamic models, our framework approximates a system transition function via non-parametric learning only using a small number of exploratory actions (ca. 10). The approximated function is then integrated with model predictive control to provide precise pushing manipulation. Furthermore, we show that the approximated system transition functions can be robustly transferred across novel objects while being online updated to continuously improve the manipulation accuracy. Through extensive experiments on a real robot platform with a set of novel objects and comparing against a state-of-the-art baseline, we show that the proposed unified framework is a light-weight and highly effective approach to enable precise pushing manipulation all by itself. Our evaluation results illustrate that the system can robustly ensure millimeter-level precision and can straightforwardly work on any novel object.

Interactive Robot-Environment Self-Calibration via Compliant Exploratory Actions

Calibrating robots into their workspaces is crucial for manipulation tasks. Existing calibration techniques often rely on sensors external to the robot (cameras, laser scanners, etc.) or specialized tools. This reliance complicates the calibration process and increases the costs and time requirements. Furthermore, the associated setup and measurement procedures require significant human intervention, which makes them more challenging to operate. Using the built-in force-torque sensors, which are nowadays a default component in collaborative robots, this work proposes a self-calibration framework where robot-environmental spatial relations are automatically estimated through compliant exploratory actions by the robot itself. The self-calibration approach converges, verifies its own accuracy, and terminates upon completion, autonomously purely through interactive exploration of the environment's geometries. Extensive experiments validate the effectiveness of our self-calibration approach in accurately establishing the robot-environment spatial relationships without the need for additional sensing equipment or any human intervention.

Wearable Roller Rings to Enable Robot Dexterous In-Hand Manipulation through Active Surfaces

In-hand manipulation is a crucial ability for reorienting and repositioning objects within grasps. The main challenges are not only the complexity in the computational models, but also the risks of grasp instability caused by active finger motions, such as rolling, sliding, breaking, and remaking contacts. Based on the idea of manipulation without lifting a finger, this paper presents the development of Roller Rings (RR), a modular robotic attachment with active surfaces that is wearable by both robot and human hands. By installing and angling the RRs on grasping systems, such that their spatial motions are not co-linear, we derive a general differential motion model for the object actuated by the active surfaces. Our motion model shows that complete in-hand manipulation skill sets can be provided by as few as only 2 RRs through non-holonomic object motions, while more RRs can enable enhanced manipulation dexterity with fewer motion constraints. Through extensive experiments, we wear RRs on both a robot hand and a human hand to evaluate their manipulation capabilities, and show that the RRs can be employed to manipulate arbitrary object shapes to provide dexterous in-hand manipulation.

RISeg: Robot Interactive Object Segmentation via Body Frame-Invariant Features

In order to successfully perform manipulation tasks in new environments, such as grasping, robots must be proficient in segmenting unseen objects from the background and/or other objects. Previous works perform unseen object instance segmentation (UOIS) by training deep neural networks on large-scale data to learn RGB/RGB-D feature embeddings, where cluttered environments often result in inaccurate segmentations. We build upon these methods and introduce a novel approach to correct inaccurate segmentation, such as under-segmentation, of static image-based UOIS masks by using robot interaction and a designed body frame-invariant feature. We demonstrate that the relative linear and rotational velocities of frames randomly attached to rigid bodies due to robot interactions can be used to identify objects and accumulate corrected object-level segmentation masks. By introducing motion to regions of segmentation uncertainty, we are able to drastically improve segmentation accuracy in an uncertainty-driven manner with minimal, non-disruptive interactions (ca. 2-3 per scene). We demonstrate the effectiveness of our proposed interactive perception pipeline in accurately segmenting cluttered scenes by achieving an average object segmentation accuracy rate of 80.7%, an increase of 28.2% when compared with other state-of-the-art UOIS methods.

Non-Parametric Self-Identification and Model Predictive Control of Dexterous In-Hand Manipulation

Building hand-object models for dexterous in-hand manipulation remains a crucial and open problem. Major challenges include the difficulty of obtaining the geometric and dynamical models of the hand, object, and time-varying contacts, as well as the inevitable physical and perception uncertainties. Instead of building accurate models to map between the actuation inputs and the object motions, this work proposes to enable the hand-object systems to continuously approximate their local models via a self-identification process where an underlying manipulation model is estimated through a small number of exploratory actions and non-parametric learning. With a very small number of data points, as opposed to most data-driven methods, our system self-identifies the underlying manipulation models online through exploratory actions and non-parametric learning. By integrating the self-identified hand-object model into a model predictive control framework, the proposed system closes the control loop to provide high accuracy in-hand manipulation. Furthermore, the proposed self-identification is able to adaptively trigger online updates through additional exploratory actions, as soon as the self-identified local models render large discrepancies against the observed manipulation outcomes. We implemented the proposed approach on a sensorless underactuated Yale Model O hand with a single external camera to observe the object's motion. With extensive experiments, we show that the proposed self-identification approach can enable accurate and robust dexterous manipulation without requiring an accurate system model nor a large amount of data for offline training.

Kinodynamic Rapidly-exploring Random Forest for Rearrangement-Based Nonprehensile Manipulation

Rearrangement-based nonprehensile manipulation still remains as a challenging problem due to the high-dimensional problem space and the complex physical uncertainties it entails. We formulate this class of problems as a coupled problem of local rearrangement and global action optimization by incorporating free-space transit motions between constrained rearranging actions. We propose a forest-based kinodynamic planning framework to concurrently search in multiple problem regions, so as to enable global exploration of the most task-relevant subspaces, while facilitating effective switches between local rearranging actions. By interleaving dynamic horizon planning and action execution, our framework can adaptively handle real-world uncertainties. With extensive experiments, we show that our framework significantly improves the planning efficiency and manipulation effectiveness while being robust against various uncertainties.

Self-Supervised Unseen Object Instance Segmentation via Long-Term Robot Interaction

We introduce a novel robotic system for improving unseen object instance segmentation in the real world by leveraging long-term robot interaction with objects. Previous approaches either grasp or push an object and then obtain the segmentation mask of the grasped or pushed object after one action. Instead, our system defers the decision on segmenting objects after a sequence of robot pushing actions. By applying multi-object tracking and video object segmentation on the images collected via robot pushing, our system can generate segmentation masks of all the objects in these images in a self-supervised way. These include images where objects are very close to each other, and segmentation errors usually occur on these images for existing object segmentation networks. We demonstrate the usefulness of our system by fine-tuning segmentation networks trained on synthetic data with real-world data collected by our system. We show that, after fine-tuning, the segmentation accuracy of the networks is significantly improved both in the same domain and across different domains. In addition, we verify that the fine-tuned networks improve top-down robotic grasping of unseen objects in the real world.

Rearrangement-Based Manipulation via Kinodynamic Planning and Dynamic Planning Horizons

Robot manipulation in cluttered environments often requires complex and sequential rearrangement of multiple objects in order to achieve the desired reconfiguration of the target objects. Due to the sophisticated physical interactions involved in such scenarios, rearrangement-based manipulation is still limited to a small range of tasks and is especially vulnerable to physical uncertainties and perception noise. This paper presents a planning framework that leverages the efficiency of sampling-based planning approaches, and closes the manipulation loop by dynamically controlling the planning horizon. Our approach interleaves planning and execution to progressively approach the manipulation goal while correcting any errors or path deviations along the process. Meanwhile, our framework allows the definition of manipulation goals without requiring explicit goal configurations, enabling the robot to flexibly interact with all objects to facilitate the manipulation of the target ones. With extensive experiments both in simulation and on a real robot, we evaluate our framework on three manipulation tasks in cluttered environments: grasping, relocating, and sorting. In comparison with two baseline approaches, we show that our framework can significantly improve planning efficiency, robustness against physical uncertainties, and task success rate under limited time budgets.