Human-Robot Collaboration (HRC) scenarios demand computationally efficient frameworks that enable natural and safe actions and interactions in shared workspaces. To address this, we propose a novel framework that utilises interactive Gaussian Process (GP) distance fields applying Riemannian Motion Policies (RMP) for key HRC functionality. Unlike traditional Euclidean distance field methods, our framework provides continuous and differentiable distance fields resulting in smooth collision avoidance, efficient updates in dynamic scenes and readily available surface information such as normal vectors and curvature. By leveraging RMPs, our framework supports fast, reactive motion generation, utilising both the distance and gradient fields generated by the GP model. In addition, we propose a Hessian-based normal vector estimation technique that elegantly leverages the GP's second-order derivative information which we utilise for object manipulation. We demonstrate the versatility of our CPU-only system in common HRC scenarios where a collaborative robot (cobot) interacts safely and naturally with a human and performs grasping actions in a dynamic environment. Our framework offers an open-source11https://uts-ri.github.io/IDMP-RMP/, comprehensive and low-computational resource solution for HRC, making it an ideal tool for conducting a wide range of user studies. By providing a continuous and differentiable distance field and combining motion generation, obstacle avoidance, and object manipulation within a single system, we aim to broaden the scope and accessibility of HRC research in real dynamic environments.

Robotic manipulator applications often require efficient online motion planning. When completing multiple tasks, sequence order and choice of goal configuration can have a drastic impact on planning performance. This is well known as the robot task sequencing problem (RTSP). Existing general-purpose RTSP algorithms are susceptible to producing poor-quality solutions or failing entirely when available computation time is restricted. We propose a new multiquery task sequencing method designed to operate in semistructured environments with a combination of static and nonstatic obstacles. Our method intentionally trades off workspace generality for planning efficiency. Given a user-defined task space with static obstacles, we compute a subspace decomposition. The key idea is to establish approximate isometries known as$\epsilon$-Gromov-Hausdorff approximations that identify points that are close to one another in both task and configuration space. Importantly, we prove bounded suboptimality guarantees on the lengths of paths within these subspaces. These bounding relations further imply that paths within the same subspace can be smoothly concatenated, which we show is useful for determining efficient task sequences. We evaluate our method with several kinematic configurations in a complex simulated environment, achieving up to 3× faster motion planning and 5× lower maximum trajectory jerk compared to baselines.

In this paper, we propose a robot skill-learning method that facilitates fast adaption to new tasks online. Our method is based on a hybrid learning from demonstration and reinforcement learning approach, which seeds learning with a compact and structured skill model, leading to efficient and stable behaviours. To facilitate fast skill adaption, we propose a bootstrapped learning framework that learns a policy for adapting a skill model across a wide range of initial conditions in simulation. This policy is then used to bootstrap a refinement process that quickly adapts the learnt skill model to new initial conditions in a few learning iterations. Our refined skill model is designed to be deployable on hardware and can correct for discrepancies between the simulation and the real world. Furthermore, we propose a novel method for constraining policy exploration to promising trajectories, which is crucial for enabling manipulation in complex environments. We evaluate our framework in simulation and hardware in multiple environments with varying task complexity. We showcase that compared to the state-of-the-art, which achieves an average success rate of only 56.6% across three different tasks of varying difficulty, our algorithm significantly outperforms it with an average success rate of 90%.

Robotic additive manufacturing has the potential to replace traditional production techniques with more flexible, capable and efficient methods. However, this potential has not yet been realized due to the intrinsic physical limitations of extruders. Thus improved speed and efficiency lies in coordinated fabrication by multiple extruders. In this paper, we propose a framework for utilizing multiple extruders to collaboratively fabricate objects in a shared workspace. In contrast to related work, we make use of a Reeb decomposition method of the input model, which dramatically reduces the search space over feasible toolpaths but still results in highly effective allocation of model components to each extruder. We demonstrate superior performance of our approach in simulation as well as in hardware with a two-robot arm extruder system. When compared to a single extruder approach over a benchmark of 14 models, our method achieves a mean improvement of 72% in extruder utilization. When compared to two additional toolpath planning methods for multiple extruders, only our method is able to successfully complete a valid toolpath on all models. Prior methods fail on more than half of our benchmark due to reaching deadlocked states during toolpath planning. On the models for which all methods succeed, our approach achieves mean utilization improvements of 132% over zone-blocking and 12% over contour greedy. We demonstrate that our approach is also suitable for models with non-planar slicings, which yield further improvements in extruder utilization. For more results and information see: https://sites.google.com/view/multi-arm-reeb.

Learning from demonstration (LfD) has the potential to greatly increase the applicability of robotic manipulators in modern industrial applications. Recent progress in LfD methods have put more emphasis in learning robustness than in guiding the demonstration itself in order to improve robustness. The latter is particularly important to consider when the target system reproducing the motion is structurally different to the demonstration system, as some demonstrated motions may not be reproducible. In light of this, this paper introduces a new guided learning from demonstration paradigm where an interactive graphical user interface (GUI) guides the user during demonstration, preventing them from demonstrating non-reproducible motions. The key aspect of our approach is determining the space of reproducible motions based on a motion planning framework which finds regions in the task space where trajectories are guaranteed to be of bounded length. We evaluate our method on two different setups with a six-degree-of-freedom (DOF) UR5 as the target system. First our method is validated using a seven-DOF Sawyer as the demonstration system. Then an extensive user study is carried out where several participants are asked to demonstrate, with and without guidance, a mock weld task using a hand held tool tracked by a VICON system. With guidance users were able to always carry out the task successfully in comparison to only 44% of the time without guidance.

This paper presents an active visual-inertial mapping framework with points and planes. The key aspect of the proposed framework is a novel probabilistic plane extraction with its associated model for estimation. The approach allows the extraction of plane parameters and their uncertainties based on a modified version of PlaneRCNN [1]. The extracted probabilistic plane features are fused with point features in order to increase the robustness of the estimation system in texture-less environments, where algorithms based on points alone would struggle. A visual-inertial framework based on Iterative Extended Kalman filter (IEKF) is used to demonstrate the approach. The IEKF equations are customized through a measurement extrapolation method, which enables the estimation to handle the delay introduced by the neural network inference time systematically. The system is encompassed within an active mapping framework, based on Informative Path Planning to find the most informative path for minimizing map uncertainty in visual-inertial systems. The results from the conducted experiments with a stereo/IMU system mounted on a robotic arm show that introducing planar features to the map, in order to complement the point features in the state estimation, improves robustness in texture-less environments.

Dormant pruning is a major cost component of fresh market tree fruit production, nearly equal in scale to harvesting the fruit. However, relatively little focus has been given to the problem of pruning trees autonomously. In this paper, we introduce a robotic system consisting of an industrial manipulator, an eye-in-hand RGB-D camera configuration, and a custom pneumatic cutter. The system is capable of planning and executing a sequence of cuts while making minimal assumptions about the environment. We leverage a novel planning framework designed for high-throughput operation which builds upon previous work to reduce motion planning time and sequence cut points intelligently. In end-to-end experiments with a set of ten different branch configurations, the system achieved a high success rate in plan execution and a 1.5x speedup in throughput versus a baseline planner, representing a significant step towards the goal of practical implementation of robotic pruning.

We consider the problem of reconstructing regions of interest of a scene using multiple robot arms and RGB-D sensors. This problem is motivated by a variety of applications, such as precision agriculture and infrastructure inspection. A viewpoint evaluation function is presented that exploits predicted observations and the geometry of the scene. A recently proposed non-myopic planning algorithm, Decentralised Monte Carlo tree search, is used to coordinate the actions of the robot arms. Motion planning is performed over a navigation graph that considers the high-dimensional configuration space of the robot arms. Extensive simulated experiments are carried out using real sensor data and then validated on hardware with two robot arms. Our proposed targeted information gain planner is compared to state-of-the-art baselines and outperforms them in every measured metric. The robots quickly observe and accurately detect fruit in a trellis structure, demonstrating the viability of the approach for real-world applications.