Leveraging gesture-based controls within a remote interface for automated material handling operations presents a promising avenue to optimize experience in outdoor environments. We present a gesture-based interactive system designed to facilitate collaboration between a human operator and a semi-autonomous forklift in material handling processes. Our interface supports commands that include starting the forklift, defining the loading/unloading area and the amount of pallets that need to be transported. We tested and compared the proposed solution to a touch-based User Interface in a field study to explore its effectiveness and identify potential challenges for implementation and adoption in industrial real-world settings.

Open-world robotic tasks such as autonomous driving pose significant challenges to robot control due to unknown and unpredictable events that disrupt task performance. Neural network-based reinforcement learning (RL) techniques (like DQN, PPO, SAC, etc.) struggle to adapt in large domains and suffer from catastrophic forgetting. Hybrid planning and RL approaches have shown some promise in handling environmental changes but lack efficiency in accommodation speed. To address this limitation, we propose an enhanced hybrid system with a nested hierarchical action abstraction that can utilize previously acquired skills to effectively tackle unexpected novelties. We show that it can adapt faster and generalize better compared to state-of-the-art RL and hybrid approaches, significantly improving robustness when multiple environmental changes occur at the same time.

In dynamic open-world environments, agents continually face new challenges due to sudden and unpredictable novelties, hindering Task and Motion Planning (TAMP) in autonomous systems. We introduce a novel TAMP architecture that integrates symbolic planning with reinforcement learning to enable autonomous adaptation in such environments, operating without human guidance. Our approach employs symbolic goal representation within a goal-oriented learning framework, coupled with planner-guided goal identification, effectively managing abrupt changes where traditional reinforcement learning, re-planning, and hybrid methods fall short. Through sequential novelty injections in our experiments, we assess our method’s adaptability to continual learning scenarios. Extensive simulations conducted in a robotics domain corroborate the superiority of our approach, demonstrating faster convergence to higher performance compared to traditional methods. The success of our framework in navigating diverse novelty scenarios within a continuous domain underscores its potential for critical real-world applications.

This paper presents a path planning concept for trucks with trailers with kingpin hitching. This system is nonholonomic, has no flat output and is not stable in backwards driving direction. These properties are major challenges for path planning. The presented approach concentrates on the loading bay scenario. The considered task is to plan a path for the truck-trailer system from a start to a specified target configuration corresponding to the loading bay. Thereby, close distances to obstacles and multiple driving direction changes have to be handled. Furthermore, a so-called jackknife position has to be avoided. In a first step, an initial path is planned from the target to the start configuration using a tree-based path planner. Afterwards this path is refined locally by solving an optimal control problem. Due to the local nature of the planner, heuristic rules for direction changes are formulated. The performance of the proposed path planner is evaluated in simulation studies.

This paper presents a fast optimization based algorithm for car parking. The challenge arises from the non-holonomic characteristics of the car and the close distance to the obstacles. The presented approach utilizes the Minkowski sum to account for obstacle avoidance. The geometric path planning problem is decoupled from the kinematic problem and discretized with respect to the path parameter by means of a Runge-Kutta discretization. For the discrete path segments, an optimization problem is formulated to calculate the path independent of the parking scenario. This static optimization problem can be solved numerically in a very efficient way. The performance of the algorithm is evaluated in several simulation scenarios.