We study robot construction problems where multiple autonomous robots rearrange stacks of prefabricated blocks to build stable structures. These problems are challenging due to ramifications of actions, true concurrency, and requirements of supportedness of blocks by other blocks and stability of the structure at all times. We propose a formal hybrid planning framework to solve a wide range of robot construction problems, based on Answer Set Programming. This framework not only decides for a stable final configuration of the structure, but also computes the order of manipulation tasks for multiple autonomous robots to build the structure from an initial configuration, while simultaneously ensuring the stability, supportedness and other desired properties of the partial construction at each step of the plan. We prove the soundness and completeness of our formal method with respect to these properties. We introduce a set of challenging robot construction benchmark instances, including bridge building and stack overhanging scenarios, discuss the usefulness of our framework over these instances, and demonstrate the applicability of our method using a bimanual Baxter robot.
Note that for sophisticated constructions that involve temporary scaffolding, counterweights, and subassemblies, it is required to express ramifications of actions as well as true concurrency. However, expressing ramifications directly by simple Strips operators is not possible [Theorem 3] thiebauxHN05 due to lack of logical inference. Also, expressing true concurrency is not possible unless the description is extended with exponential number of new operators, where each operator characterizes a concurrent action. Due to these theoretical results, other studies @cite_27 @cite_13 @cite_44 that rely on simple Strips operators, do not present general methods for such sophisticated constructions either. It is important to note that these methods do not cover sophisticated structures, like bridges or overhangs, since objects are not necessarily placed on support areas of other objects. Such sophisticated structures require definition of transitive closure to ensure supportedness or connectedness. Transitive closure is not definable in first-order logic [Theorem 5] Fagin75 ; it is not directly supported by Strips either @cite_25 .
@cite_44 @cite_27 @cite_13 @cite_25
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Joint symbolic and geometric planning is one of the core challenges in robotics. We address the problem of multi-agent cooperative manipulation, where we aim for jointly optimal paths for all agents and over the full manipulation sequence. This joint optimization problem can be framed as a logic-geometric program. Existing solvers lack several features (such as consistently handling kinematic switches) and efficiency to handle the cooperative manipulation domain. We propose a new approximate solver scheme, combining ideas from branch-and-bound and MCTS and exploiting multiple levels of bounds to better direct the search. We demonstrate the method in a scenario where a Baxter robot needs to help a human to reach for objects. On the path to full autonomy, robotic agents have to learn how to manipulate their environments for their benefit. In particular, the ability to design structures that are functional in overcoming challenges is imperative. The problem of automated design of functional structures (ADFS) addresses the question of whether the objects in the environment can be placed in a useful configuration. In this work, we first make the observation that the ADFS problem represents a class of problems in high dimensional, continuous spaces that can be broken down into simpler subproblems with semantically meaningful actions. Next, we propose a framework where discrete actions that induce constraints can partition the solution space effectively. Subsequently, we solve the original class of problems by searching over the available actions, where the evaluation criteria for the search is the feasibility test of the accumulated constraints. We prove that with a sound feasibility test, our algorithm is complete. Additionally, we argue that a convexity requirement on the constraints leads to significant efficiency gains. Finally, we present successful results to the ADFS problem. We present an algorithm that enables a humanoid robot to reason about its environment and use the available objects to build bridges, stairs and lever-fulcrum systems. Facing a challenge that is otherwise intractable, such as climbing a height or moving a heavy object, the proposed planner reasons about the physical limitations of the robot to design functional structures. Inducing constraints on the space of possible designs within a classical planning framework, the algorithm outputs feasible structures that can be used towards accomplishing goals. We present results in dynamic simulation with Golem Hubo, walking on a bridge to cross a hazardous area, and in real-world with Golem Krang, overturning 100 kg loads and pushing 240 kg obstacles. There is controversy as to whether explicit support for pddl-like axioms and derived predicates is needed for planners to handle real-world domains effectively. Many researchers have deplored the lack of precise semantics for such axioms, while others have argued that it might be best to compile them away. We propose an adequate semantics for pddl axioms and show that they are an essential feature by proving that it is impossible to compile them away if we restrict the growth of plans and domain descriptions to be polynomial. These results suggest that adding a reasonable implementation to handle axioms inside the planner is beneficial for the performance. Our experiments confirm this suggestion.