Ground robots which are able to navigate a variety of terrains are needed in many domains. One of the key aspects is the capability to adapt to the ground structure, which can be realized through movable body parts coming along with additional degrees of freedom (DoF). However, planning respective locomotion is challenging since suitable representations result in large state spaces. Employing an additional abstract representation—which is coarser, lower-dimensional, and semantically enriched—can support the planning. While a desired robot representation and action set of such an abstract representation can be easily defined, the cost function requires large tuning efforts. We propose a method to represent the cost function as a CNN. Training of the network is done on generated artificial data, while it generalizes well to the abstraction of real world scenes. We further apply our method to the problem of search-based planning of hybrid driving-stepping locomotion. The abstract representation is used as a powerful informed heuristic which accelerates planning by multiple orders of magnitude.
To summarize, learning-based planning approaches can handle local problems with limited state space sizes quickly without performing extensive searches. In contrast, traditional planning approaches show good goal-directed behavior but might get stuck in extensive searches for complex high-dimensional problems. Hence, it promising to combine these approaches and merge the advantages of both. Faust al @cite_21 use a reinforcement learning agent to learn short-range, point-to-point navigation policies for 2D and 3D action spaces which capture the robot dynamic and task constraint without considering the large-scale topology. Sampling-based planning is used to plan waypoints which give the planning a long-range goal-directed behavior.
@cite_21
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Imitation learning of sequential, goal-directed behavior by standard supervised techniques is often difficult. We frame learning such behaviors as a maximum margin structured prediction problem over a space of policies. In this approach, we learn mappings from features to cost so an optimal policy in an MDP with these cost mimics the expert's behavior. Further, we demonstrate a simple, provably efficient approach to structured maximum margin learning, based on the subgradient method, that leverages existing fast algorithms for inference. Although the technique is general, it is particularly relevant in problems where A* and dynamic programming approaches make learning policies tractable in problems beyond the limitations of a QP formulation. We demonstrate our approach applied to route planning for outdoor mobile robots, where the behavior a designer wishes a planner to execute is often clear, while specifying cost functions that engender this behavior is a much more difficult task. Model-free reinforcement learning has recently been shown to be effective at learning navigation policies from complex image input. However, these algorithms tend to require large amounts of interaction with the environment, which can be prohibitively costly to obtain on robots in the real world. We present an approach for efficiently learning goal-directed navigation policies on a mobile robot, from only a single coverage traversal of recorded data. The navigation agent learns an effective policy over a diverse action space in a large heterogeneous environment consisting of more than 2km of travel, through buildings and outdoor regions that collectively exhibit large variations in visual appearance, self-similarity, and connectivity. We compare pretrained visual encoders that enable precomputation of visual embeddings to achieve a throughput of tens of thousands of transitions per second at training time on a commodity desktop computer, allowing agents to learn from millions of trajectories of experience in a matter of hours. We propose multi- ple forms of computationally efficient stochastic augmentation to enable the learned policy to generalise beyond these precomputed embeddings, and demonstrate successful deployment of the learned policy on the real robot without fine tuning, despite environmental appearance differences at test time. The dataset and code required to reproduce these results and apply the technique to other datasets and robots is made publicly available at rl-navigation.github.io deployable . Long-term activity forecasting deals with the problem of predicting how an agent will complete a full activity, defined as a continuous trajectory and a discrete sequence of sub-actions. While previous data-driven methods only dealt with forecasting 2D trajectories, we present a method that leverages common sense prior knowledge and minimal data. In order to forecast the trajectories, we learn a policy function that maps from states to actions the agent should perform next. Through the use of deep reinforcement learning, our method is able to learn a highly non-linear mapping from agent states to actions. We develop the first forecasting framework that uses ego-centric video input, which is an optimal vantage point for understanding human activities over large spaces. Given an annotated first person video sequence for the activity, we construct a 3D point cloud of the environment and activity paths through 3D space. Based on a limited number of examples, we use reinforcement learning to derive a policy for the entire environment, even for areas that have never been visited during the demonstrated examples. We explore the use of deep reinforcement learning to recover a direct mapping from environmental features to best action. Our approach makes it possible to combine a high dimensional continuous state (namely the local point could density surrounding the agent) with a discrete state portion (action stage of an activity) into a single state for behavior forecasting. The result is a policy that generalizes very well from only a few activity samples. We validate our approach on our First-Person Office Behavior Dataset and show that our method of encoding more prior knowledge leads to an increase in forecasting accuracy. We also demonstrate that the deep reinforcement learning approach is able to achieve higher forecasting accuracy than the traditional alternatives. Enabling robots to accomplish sophisticated tasks requires enhancing their capability to plan at multiple levels of discrete and continuous abstractions. Toward this goal, the proposed approach couples the ability of sampling-based motion planning to handle the complexity arising from high-dimensional robotic systems, nonlinear dynamics, and collision avoidance with the ability of discrete planning to handle discrete specifications. The approach makes it possible to specify tasks via Linear Temporal Logic (LTL) and automatically computes collision-free and dynamically-feasible motions that enable the robot to carry out assigned tasks. While discrete planning guides sampling-based motion planning, the latter feeds back information to further refine the guide and advance the search. Sampling is also used in the discrete space to shorten the length of the discrete plans and to expand the search toward new discrete states. Experiments with high-dimensional dynamical robot models performing various LTL tasks show significant computational speedups over related work.