We are interested in enabling robots to solve difficult pick-and-place manipulation tasks in cluttered and constrained environments. If the robot does not have collision-free access to the object-of-interest (OoI) which it intends to grasp and extract from the workspace, it must reason about which movable objects to rearrange, where to move them, and how it may do so. In recent work [1] we introduced E-M4M, a graph search-based solver for solving such Manipulation tasks Among Movable Objects (MAMO). In this paper we make several improvements to E-M4M – we introduce the use of prehensile or pick-and-place rearrangement actions in addition to pushes; we show that by running it as a depth-first search improves performance; we show how the search can be run "eagerly lazily" to only simulate actions in a physics-based simulator when necessary; finally we relax the assumption that we require perfect knowledge of the physical properties of objects (mass and coefficient of friction in particular). The improved version of E-M4M presented in this paper, I-M4M, is a faster and more versatile MAMO solver with a rich action space. We discuss the impact of the improvements we make in an extensive simulation study and show previously unachievable results on a real-world PR2 robot.

Real-world manipulation problems in heavy clutter require robots to reason about potential contacts with objects in the environment. We focus on pick-and-place style tasks to retrieve a target object from a shelf where some ‘movable’ objects must be rearranged in order to solve the task. In particular, our motivation is to allow the robot to reason over and consider non-prehensile rearrangement actions that lead to complex robot-object and object-object interactions where multiple objects might be moved by the robot simultaneously, and objects might tilt, lean on each other, or topple. To support this, we query a physics-based simulator to forward simulate these interaction dynamics which makes action evaluation during planning computationally very expensive. To make the planner tractable, we establish a connection between the domain of Manipulation Among Movable Objects and Multi-Agent Pathfinding that lets us decompose the problem into two phases our M4M algorithm iterates over. First we solve a multi-agent planning problem that reasons about the configurations of movable objects but does not forward simulate a physics model. Next, an arm motion planning problem is solved that uses a physics-based simulator but does not search over possible configurations of movable objects. We run simulated and real-world experiments with the PR2 robot and compare against relevant baseline algorithms. Our results highlight that M4M generates complex 3D interactions, and solves at least twice as many problems as the baselines with competitive performance.

This paper presents a comprehensive analysis of the performance of Fortran, C, Python, and Julia based GPU accelerated meshfree solvers for compressible flows. The programming model CUDA is used to develop the GPU codes. The meshfree solver is based on the least squares kinetic upwind method with entropy variables (q-LSKUM). To measure the performance of baseline codes, benchmark calculations are performed. The codes are then profiled to investigate the differences in their performance. Analysing various performance metrics for the computationally expensive flux residual kernel helped identify various bottlenecks in the codes. To resolve the bottlenecks, several optimisation techniques are employed. Post optimisation, the performance metrics have improved significantly, with the C GPU code exhibiting the best performance.

Heuristic search-based motion planning algorithms typically discretise the search space in order to solve the shortest path problem. Their performance is closely related to this discretisation. A fine discretisation allows for better approximations of the continuous search space, but makes the search for a solution more computationally costly. A coarser resolution might allow the algorithms to find solutions quickly at the expense of quality. For large state spaces, it can be beneficial to search for solutions across multiple resolutions even though defining the discretisations is challenging. The recently proposed algorithm Multi-Resolution A* (MRA*) searches over multiple resolutions. It traverses large areas of obstacle-free space and escapes local minima at a coarse resolution. It can also navigate so-called narrow passageways at a finer resolution. In this work, we develop AMRA*, an anytime version of MRA*, AMRA* tries to find a solution quickly using the coarse resolution as much as possible. It then refines the solution by relying on the fine resolution to discover better paths that may not have been available at the coarse resolution. In addition to being anytime, AMRA* can also leverage information sharing between multiple heuristics. We prove that AMRA* is complete and optimal (in-the-limit of time) with respect to the finest resolution. We show its performance on 2D grid navigation and 4D kinodynamic planning problems.

Path planning for robotic coverage is the task of determining a collision-free robot trajectory that observes all points of interest in an environment. Robots employed for such tasks are often capable of exercising active control over onboard observational sensors during navigation. We address the problem of planning robot and sensor trajectories that maximize information gain in such tasks, where the robot needs to cover points of interest with its sensor footprint. Search-based planners in general guarantee completeness and provable bounds on sub-optimality with respect to an underlying graph discretization. However, searching for kinodynamically feasible paths in the joint space of robot and sensor state variables with standard search is computationally expensive. We propose two alternative search-based approaches to this problem. The first solves for robot and sensor trajectories independently in decoupled state spaces while maintaining a history of sensor headings during the search. The second is a two-step approach that first quickly computes a solution in decoupled state spaces and then refines it by searching its local neighborhood in the joint space for a better solution. We evaluate our approaches in simulation with a kinodynamically constrained unmanned aerial vehicle performing coverage over a 2D environment and show their benefits.

Robot manipulation in cluttered scenes often requires contact-rich interactions with objects. It can be more economical to interact via non-prehensile actions, for example, push through other objects to get to the desired grasp pose, instead of deliberate prehensile rearrangement of the scene. For each object in a scene, depending on its properties, the robot may or may not be allowed to make contact with, tilt, or topple it. To ensure that these constraints are satisfied during non-prehensile interactions, a planner can query a physics-based simulator to evaluate the complex multi-body interactions caused by robot actions. Unfortunately, it is infeasible to query the simulator for thousands of actions that need to be evaluated in a typical planning problem as each simulation is time-consuming. In this work, we show that (i) manipulation tasks (specifically pick-and-place style tasks from a tabletop or a refrigerator) can often be solved by restricting robot-object interactions to adaptive motion primitives in a plan, (ii) these actions can be incorporated as subgoals within a multi-heuristic search framework, and (iii) limiting interactions to these actions can help reduce the time spent querying the simulator during planning by up to 40× in comparison to baseline algorithms. Our algorithm is evaluated in simulation and in the real-world on a PR2 robot using PyBullet as our physics-based simulator. Supplementary video: https://youtu.be/ABQc7JbeJPM.

Traditional planning and control methods could fail to find a feasible trajectory for an autonomous vehicle to execute amongst dense traffic on roads. This is because the obstacle-free volume in spacetime is very small in these scenarios for the vehicle to drive through. However, that does not mean the task is infeasible since human drivers are known to be able to drive amongst dense traffic by leveraging the cooperativeness of other drivers to open a gap. The traditional methods fail to take into account the fact that the actions taken by an agent affect the behaviour of other vehicles on the road. In this work, we rely on the ability of deep reinforcement learning to implicitly model such interactions and learn a continuous control policy over the action space of an autonomous vehicle. The application we consider requires our agent to negotiate and open a gap in the road in order to successfully merge or change lanes. Our policy learns to repeatedly probe into the target road lane while trying to find a safe spot to move in to. We compare against two model-predictive control-based algorithms and show that our policy outperforms them in simulation. As part of this work, we introduce a benchmark for driving in dense traffic for use by the community.

Sampling-based approaches are often favored in robotics for high-dimensional motion planning for their fast exploration of the search space. However, at best they offer asymptotic guarantees on solution quality due to their inherent stochasticity. While planning, the majority of effort is often spent near the start and goal configurations with a large amount of free space in between. Bidirectional approaches such as RRT-Connect exploit this fact by greedily extending and connecting search frontiers that simultaneously propagate from the start and goal configurations of a planning problem. In this work, we use such an extend operator for bidirectional heuristic search-based planners, which typically struggle with high-dimensionality. In doing so, we address the difficulty that these bidirectional planners face with connecting frontiers of both search efforts while providing suboptimality bounds on solution quality. We validate our simple approach on high-dimensional manipulation tasks, demonstrating significantly reduced search effort when compared against other popular bidirectional algorithms, both search-based and sampling. Our algorithm maintains theoretical guarantees on suboptimality and completeness for a given resolution. In addition, the solutions found by our planner are of higher quality compared to those found by the other baseline algorithms.

The ability of autonomous mobile robots to react to and recover from potential failures of on-board systems is an important area of ongoing robotics research. With increasing emphasis on robust systems and long-term autonomy, mobile robots must be able to respond safely and intelligently to dangerous situations. Recent developments in computer vision have made autonomous vision based navigation possible. However, vision systems are known to be imperfect and prone to failure due to variable lighting, terrain changes, and other environmental variables. We describe a system for learning simple failure recovery maneuvers based on experience. This involves both recognizing when the vision system is prone to failure, and associating failures with appropriate responses that will most likely help the robot recover. We implement this system on an autonomous quadrotor and demonstrate that behaviors learned with our system are effective in recovering from situational perception failure, thereby improving reliability in cluttered and uncertain environments.

In this paper we discuss the development of a novel rapid prototyping method that makes the process of creating tangible electronic artifacts faster and easier. This method makes use of a new paper-like material that can be given any form just by hand or by using other stationary objects. This material changes its stiffness and becomes harder soon after the modeling process. Furthermore, this material can be integrated within electronic circuits using magnetic connectors and silver. The discussed method has been conceptualized using a People-Centered Design approach while its implementation has been led by an engineering approach. Both, the conceptualization process as well as the implementation of the discussed rapid prototyping method have been detailed in this paper along with example scenarios where the said implementation could be useful.