OpenRaiser/Intern-Atlas huggingface dataset:
Coordination of Multiple Mobile Manipulators for Ordered Sorting of Cluttered Objects
Venue: Conference IROS 2023
Year: 2023

We present a coordination method for multiple mobile manipulators to sort objects in clutter. We consider the object rearrangement problem in which the objects must be sorted into different groups in a particular order. In clutter, the order constraints could not be easily satisfied since some objects occlude other objects so the occluded ones are not directly accessible to the robots. Those objects occluding others need to be moved more than once to make the occluded objects accessible. Such rearrangement problems fall into the class of nonmonotone rearrangement problems which are computationally intractable. While the nonmonotone problems with order constraints are harder, involving with multiple robots requires another computation for task allocation. The proposed method first finds a sequence of objects to be sorted using a search such that the order constraint in each group is satisfied. The search can solve nonmonotone instances that require temporal relocation of some objects to access the next object to be sorted. Once a complete sorting sequence is found, the objects in the sequence are assigned to multiple mobile manipulators using a greedy allocation method. We develop four versions of the method with different search strategies. In the experiments, we show that our method can find a sorting sequence quickly (e.g., 4.6 sec with 20 objects sorted into five groups) even though the solved instances include hard nonmonotone ones. The extensive tests and the experiments in simulation show the ability of the method to solve the real-world sorting problem using multiple mobile manipulators.
Vision-Based Oxy-Fuel Torch Control for Robotic Metal Cutting
Venue: Conference IROS 2023
Year: 2023

The automation of key processes in metal cutting would substantially benefit many industries such as manufacturing and metal recycling. We present a vision-based control scheme for automated metal cutting with oxy-fuel torches, an established cutting medium in industry. The system consists of a robot equipped with a cutting torch and an eye-in-hand camera observing the scene behind a tinted visor. We develop a vision-based control algorithm to servo the torch's motion by visually observing its effects on the metal surface. As such, the vision system processes the metal surface's heat pool and computes its associated features, specifically pool convexity and intensity, which are then used for control. The operating conditions of the control problem are defined within which the stability is proven. In addition, metal cutting experiments are performed using a physical 1-DOF robot and oxy-fuel cutting equipment. Our results demonstrate the successful cutting of metal plates across three different plate thicknesses, relying purely on visual information without a priori knowledge of the thicknesses.
Material-Agnostic Shaping of Granular Materials with Optimal Transport
Venue: Conference IROS 2023
Year: 2023

From construction materials, such as sand or asphalt, to kitchen ingredients, like rice, sugar, or salt; the world is full of granular materials. Despite impressive progress in robotic manipulation, manipulating and interacting with granular material remains a challenge due to difficulties in perceiving, representing, modelling, and planning for these variable materials that have complex internal dynamics. While some prior work has looked into estimating or learning accurate dynamics models for granular materials, the literature is still missing a more abstract planning method that can be used for planning manipulation actions for granular materials with unknown material properties. In this work, we leverage tools from optimal transport and connect them to robot motion planning. We propose a heuristics-based sweep planner that does not require knowledge of the material's properties and directly uses a height map representation to generate promising sweeps. These sweeps transform granular material from arbitrary start shapes into arbitrary target shapes. We apply the sweep planner in a fast and reactive feedback loop and avoid the need for model-based planning over multiple time steps. We validate our approach with a large set of simulation and hardware experiments where we show that our method is capable of efficiently solving several complex tasks, including gathering, separating, and shaping of several types of granular materials into different target shapes.
Global Localization: Utilizing Relative Spatio-Temporal Geometric Constraints from Adjacent and Distant Cameras
Venue: Conference IROS 2023
Year: 2023

Re-localizing a camera from a single image in a previously mapped area is vital for many computer vision applications in robotics and augmented/virtual reality. In this work, we address the problem of estimating the 6 DoF camera pose relative to a global frame from a single image. We propose to leverage a novel network of relative spatial and temporal geometric constraints to guide the training of a Deep Network for localization. We employ simultaneously spatial and temporal relative pose constraints that are obtained not only from adjacent camera frames but also from camera frames that are distant in the spatio-temporal space of the scene. We show that our method, through these constraints, is capable of learning to localize when little or very sparse ground-truth 3D coordinates are available. In our experiments, this is less than 1% of available ground-truth data. We evaluate our method on 3 common visual localization datasets and show that it outperforms other direct pose estimation methods.
Optimal Cost-Preference Trade-Off Planning with Multiple Temporal Tasks
Venue: Conference IROS 2023
Year: 2023

Autonomous robots are increasingly utilized in realistic scenarios with multiple complex tasks. In these scenarios, there may be a preferred way of completing all of the given tasks, but it is often in conflict with optimal execution. Recent work studies preference-based planning, however, they have yet to extend the notion of preference to the behavior of the robot with respect to each task. In this work, we introduce a novel notion of preference that provides a generalized framework to express preferences over individual tasks as well as their relations. Then, we perform an optimal trade-off (Pareto) analysis between behaviors that adhere to the user's preference and the ones that are resource optimal. We introduce an efficient planning framework that generates Pareto-optimal plans given user's preference by extending A* search. Further, we show a method of computing the entire Pareto front (the set of all optimal trade-offs) via an adaptation of a multi-objective A* algorithm. We also present a problem-agnostic search heuristic to enable scalability. We illustrate the power of the framework on both mobile robots and manipulators. Our benchmarks show the effectiveness of the heuristic with up to 2-orders of magnitude speedup.
Value of Assistance for Mobile Agents
Venue: Conference IROS 2023
Year: 2023

Mobile robotic agents often suffer from localization uncertainty which grows with time and with the agents' movement. This can hinder their ability to accomplish their task. In some settings, it may be possible to perform assistive actions that reduce uncertainty about a robot's location. For example, in a collaborative multi-robot system, a wheeled robot can request assistance from a drone that can fly to its estimated location and reveal its exact location on the map or accompany it to its intended location. Since assistance may be costly and limited, and may be requested by different members of a team, there is a need for principled ways to support the decision of which assistance to provide to an agent and when, as well as to decide which agent to help within a team. For this purpose, we propose Value of Assistance (VOA) to represent the expected cost reduction that assistance will yield at a given point of execution. We offer ways to compute VOA based on estimations of the robot's future uncertainty, modeled as a Gaussian process. We specify conditions under which our VOA measures are valid and empirically demonstrate the ability of our measures to predict the agent's average cost reduction when receiving assistance in both simulated and real-world robotic settings.
Global Localization in Unstructured Environments Using Semantic Object Maps Built from Various Viewpoints
Venue: Conference IROS 2023
Year: 2023

We present a novel framework for global localization and guided relocalization of a vehicle in an unstructured environment. Compared to existing methods, our pipeline does not rely on cues from urban fixtures (e.g., lane markings, buildings), nor does it make assumptions that require the vehicle to be navigating on a road network. Instead, we achieve localization in both urban and non-urban environments by robustly associating and registering the vehicle's local semantic object map with a compact semantic reference map, potentially built from other viewpoints, time periods, and/or modalities. Robustness to noise, outliers, and missing objects is achieved through our graph-based data association algorithm. Further, the guided relocalization capability of our pipeline mitigates drift inherent in odometry-based localization after the initial global localization. We evaluate our pipeline on two publicly-available, real-world datasets to demonstrate its effectiveness at global localization in both non-urban and urban environments. The Katwijk Beach Planetary Rover dataset is used to show our pipeline's ability to perform accurate global localization in unstructured environments. Demonstrations on the KITTI dataset achieve an average pose error of 3.8m across all 35 localization events on Sequence 00 when localizing in a reference map created from aerial images. Compared to existing works, our pipeline is more general because it can perform global localization in unstructured environments using maps built from different viewpoints.
Exploring Social Motion Latent Space and Human Awareness for Effective Robot Navigation in Crowded Environments
Venue: Conference IROS 2023
Year: 2023

This work proposes a novel approach to social robot navigation by learning to generate robot controls from a social motion latent space. By leveraging this social motion latent space, the proposed method achieves significant improvements in social navigation metrics such as success rate, navigation time, and trajectory length while producing smoother (less jerk and angular deviations) and more anticipatory trajectories. The superiority of the proposed method is demonstrated through comparison with baseline models in various scenarios. Additionally, the concept of humans' awareness towards the robot is introduced into the social robot navigation framework, showing that incorporating human awareness leads to shorter and smoother trajectories owing to humans' ability to positively interact with the robot.
Improving Reliable Navigation Under Uncertainty via Predictions Informed by Non-Local Information
Venue: Conference IROS 2023
Year: 2023

We improve reliable, long-horizon, goal-directed navigation in partially-mapped environments by using non-locally available information to predict the goodness of temporally-extended actions that enter unseen space. Making predictions about where to navigate in general requires non-local information: any observations the robot has seen so far may provide information about the goodness of a particular direction of travel. Building on recent work in learning-augmented model-based planning under uncertainty, we present an approach that can both rely on non-local information to make predictions (via a graph neural network) and is reliable by design: it will always reach its goal, even when learning does not provide accurate predictions. We conduct experiments in three simulated environments in which non-local information is needed to perform well. In our large scale university building environment, generated from real-world floorplans to the scale, we demonstrate a 9.3\% reduction in cost-to-go compared to a non-learned baseline and a 14.9\% reduction compared to a learning-informed planner that can only use local information to inform its predictions.
SCTOMP: Spatially Constrained Time-Optimal Motion Planning
Venue: Conference IROS 2023
Year: 2023

This paper focuses on spatial time-optimal motion planning, a generalization of the exact time-optimal path following problem that allows the system to plan within a predefined space. In contrast to state-of-the-art methods, we drop the assumption that a collision-free geometric reference is given. Instead, we present a two-stage motion planning method that solely relies on a goal location and a geometric representation of the environment to compute a time-optimal trajectory that is compliant with system dynamics and constraints. To do so, the proposed scheme first computes an obstacle-free Pythagorean Hodograph parametric spline, and second solves a spatially reformulated minimum-time optimization problem. The spline obtained in the first stage is not a geometric reference, but an extension of the environment representation, and thus, time-optimality of the solution is guaranteed. The efficacy of the proposed approach is benchmarked by a known planar example and validated in a more complex spatial system, illustrating its versatility and applicability.
DS-MPEPC: Safe and Deadlock-Avoiding Robot Navigation in Cluttered Dynamic Scenes
Venue: Conference IROS 2023
Year: 2023

We present an algorithm for safe robot navigation in complex dynamic environments using a variant of model predictive equilibrium point control. We use an optimization formulation to navigate robots gracefully in dynamic environments by optimizing over a trajectory cost function at each timestep. We present a novel trajectory cost formulation that significantly reduces the conservative and deadlock behaviors and generates smooth trajectories. In particular, we propose a new collision probability function that effectively captures the risk associated with a given configuration and the time to avoid collisions based on the velocity direction. Moreover, we propose a terminal state cost based on the expected time-to-goal and time-to-collision values that helps in avoiding trajectories that could result in deadlock. We evaluate our cost formulation in multiple simulated and real-world scenarios, including narrow corridors with dynamic obstacles, and observe significantly improved navigation behavior and reduced deadlocks as compared to prior methods.
Vehicle Motion Forecasting Using Prior Information and Semantic-Assisted Occupancy Grid Maps
Venue: Conference IROS 2023
Year: 2023

Motion prediction is a challenging task for autonomous vehicles due to uncertainty in the sensor data, the non-deterministic nature of future, and complex behavior of agents. In this paper, we tackle this problem by representing the scene as dynamic occupancy grid maps (DOGMs), associating semantic labels to the occupied cells and incorporating map information. We propose a novel framework that combines deep-learning-based spatio-temporal and probabilistic approaches to predict vehicle behaviors.Contrary to the conventional OGM prediction methods, evaluation of our work is conducted against the ground truth annotations. We experiment and validate our results on real-world NuScenes dataset and show that our model shows superior ability to predict both static and dynamic vehicles compared to OGM predictions. Furthermore, we perform an ablation study and assess the role of semantic labels and map in the architecture.
Heterogeneous Coalition Formation and Scheduling with Multi-Skilled Robots
Venue: Conference IROS 2023
Year: 2023

We present an approach to task scheduling in heterogeneous multi-robot systems. In our setting, the tasks to complete require diverse skills. We assume that each robot is multi-skilled, i.e., each robot offers a subset of the possible skills. This makes the formation of heterogeneous teams (\emph{coalitions}) a requirement for task completion. We present two centralized algorithms to schedule robots across tasks and to form suitable coalitions, assuming stochastic travel times across tasks. The coalitions are dynamic, in that the robots form and disband coalitions as the schedule is executed. The first algorithm we propose guarantees optimality, but its run-time is acceptable only for small problem instances. The second algorithm we propose can tackle large problems with short run-times, and is based on a heuristic approach that typically reaches 1x-2x of the optimal solution cost.
Efficient and Feasible Robotic Assembly Sequence Planning via Graph Representation Learning
Venue: Conference IROS 2023
Year: 2023

Automatic Robotic Assembly Sequence Planning (RASP) can significantly improve productivity and resilience in modern manufacturing along with the growing need for greater product customization. One of the main challenges in realizing such automation resides in efficiently finding solutions from a growing number of potential sequences for increasingly complex assemblies. Besides, costly feasibility checks are always required for the robotic system. To address this, we propose a holistic graphical approach including a graph representation called Assembly Graph for product assemblies and a policy architecture, Graph Assembly Processing Network, dubbed GRACE for assembly sequence generation. With GRACE, we are able to extract meaningful information from the graph input and predict assembly sequences in a step-by-step manner. In experiments, we show that our approach can predict feasible assembly sequences across product variants of aluminum profiles based on data collected in simulation of a dual-armed robotic system. We further demonstrate that our method is capable of detecting infeasible assemblies, substantially alleviating the undesirable impacts from false predictions, and hence facilitating real-world deployment soon. Code and training data are available at https://github.com/DLR-RM/GRACE.
Bag All You Need: Learning a Generalizable Bagging Strategy for Heterogeneous Objects
Venue: Conference IROS 2023
Year: 2023

We introduce a practical robotics solution for the task of heterogeneous bagging, requiring the placement of multiple rigid and deformable objects into a deformable bag. This is a difficult task as it features complex interactions between multiple highly deformable objects under limited observability. To tackle these challenges, we propose a robotic system consisting of two learned policies: a rearrangement policy that learns to place multiple rigid objects and fold deformable objects in order to achieve desirable pre-bagging conditions, and a lifting policy to infer suitable grasp points for bi-manual bag lifting. We evaluate these learned policies on a real-world three-arm robot platform that achieves a 70% heterogeneous bagging success rate with novel objects. To facilitate future research and comparison, we also develop a novel heterogeneous bagging simulation benchmark that will be made publicly available.
SG-LSTM: Social Group LSTM for Robot Navigation Through Dense Crowds
Venue: Conference IROS 2023
Year: 2023

With the increasing availability and affordability of personal robots, they will no longer be confined to large corporate warehouses or factories but will instead be expected to operate in less controlled environments alongside larger groups of people. In addition to ensuring safety and efficiency, it is crucial to minimize any negative psychological impact robots may have on humans and follow unwritten social norms in these situations. Our research aims to develop a model that can predict the movements of pedestrians and perceptually-social groups in crowded environments. We introduce a new Social Group Long Short-term Memory (SG-LSTM) model that models human groups and interactions in dense environments using a socially-aware LSTM to produce more accurate trajectory predictions. Our approach enables navigation algorithms to calculate collision-free paths faster and more accurately in crowded environments. Additionally, we also release a large video dataset with labeled pedestrian groups for the broader social navigation community. We show comparisons with different metrics on different datasets (ETH, Hotel, MOT15) and different prediction approaches (LIN, LSTM, O-LSTM, S-LSTM) as well as runtime performance.
Long-Distance Gesture Recognition Using Dynamic Neural Networks
Venue: Conference IROS 2023
Year: 2023

Gestures form an important medium of communication between humans and machines. An overwhelming majority of existing gesture recognition methods are tailored to a scenario where humans and machines are located very close to each other. This short-distance assumption does not hold true for several types of interactions, for example gesture-based interactions with a floor cleaning robot or with a drone. Methods made for short-distance recognition are unable to perform well on long-distance recognition due to gestures occupying only a small portion of the input data. Their performance is especially worse in resource constrained settings where they are not able to effectively focus their limited compute on the gesturing subject. We propose a novel, accurate and efficient method for the recognition of gestures from longer distances. It uses a dynamic neural network to select features from gesture-containing spatial regions of the input sensor data for further processing. This helps the network focus on features important for gesture recognition while discarding background features early on, thus making it more compute efficient compared to other techniques. We demonstrate the performance of our method on the LD-ConGR long-distance dataset where it outperforms previous state-of-the-art methods on recognition accuracy and compute efficiency.
Efficient Constrained Multi-Agent Trajectory Optimization Using Dynamic Potential Games
Venue: Conference IROS 2023
Year: 2023

Although dynamic games provide a rich paradigm for modeling agents' interactions, solving these games for real-world applications is often challenging. Many real-world interactive settings involve general nonlinear state and input constraints that couple agents' decisions with one another. In this work, we develop an efficient and fast planner for interactive trajectory optimization in constrained setups using a constrained game-theoretical framework. Our key insight is to leverage the special structure of agents' objective and constraint functions that are common in multi-agent interactions for fast and reliable planning. More precisely, we identify the structure of agents' cost and constraint functions under which the resulting dynamic game is an instance of a constrained dynamic potential game. Constrained dynamic potential games are a class of games for which instead of solving a set of coupled constrained optimal control problems, a constrained Nash equilibrium, i.e. a Generalized Nash equilibrium, can be found by solving a single constrained optimal control problem. This simplifies constrained interactive trajectory optimization significantly. We compare the performance of our method in a navigation setup involving four planar agents and show that our method is on average 20 times faster than the state-of-the-art. We further provide experimental validation of our proposed method in a navigation setup involving two quadrotors carrying a rigid object while avoiding collisions with two humans.