COCrIP: Compliant OmniCrawler In-pipeline Robot

This paper presents a modular in-pipeline climbing robot with a novel compliant foldable OmniCrawler mechanism. The circular cross-section of the OmniCrawler module enables a holonomic motion to facilitate the alignment of the robot in the direction of bends. Additionally, the crawler mechanism provides a fair amount of traction, even on slippery surfaces. These advantages of crawler modules have been further supplemented by incorporating active compliance in the module itself which helps to negotiate sharp bends in small diameter pipes. The robot has a series of 3 such compliant foldable modules interconnected by the links via passive joints. For the desirable pipe diameter and curvature of the bends, the spring stiffness value for each passive joint is determined by formulating a constrained optimization problem using the quasi-static model of the robot. Moreover, a minimum friction coefficient value between the module-pipe surface which can be vertically climbed by the robot without slipping is estimated. The numerical simulation results have further been validated by experiments on real robot prototype

Design and optimal springs stiffness estimation of a Modular OmniCrawler in-pipe climbing Robot

This paper discusses the design of a novel compliant in-pipe climbing modular robot for small diameter pipes. The robot consists of a kinematic chain of 3 OmniCrawler modules with a link connected in between 2 adjacent modules via compliant joints. While the tank-like crawler mechanism provides good traction on low friction surfaces, its circular cross-section makes it holonomic. The holonomic motion assists it to re-align in a direction to avoid obstacles during motion as well as overcome turns with a minimal energy posture. Additionally, the modularity enables it to negotiate T-junction without motion singularity. The compliance is realized using 4 torsion springs incorporated in joints joining 3 modules with 2 links. For a desirable pipe diameter (\text{\O} 75mm), the springs' stiffness values are obtained by formulating a constraint optimization problem which has been simulated in ADAMS MSC and further validated on a real robot prototype. In order to negotiate smooth vertical bends and friction coefficient variations in pipes, the design was later modified by replacing springs with series elastic actuators (SEA) at 2 of the 4 joints.Comment: arXiv admin note: text overlap with arXiv:1704.0681

Multiagent Learning via Dynamic Skill Selection

Multiagent coordination has many real-world applications such as self-driving cars, inventory management, search and rescue, package delivery, traffic management, warehouse management, and transportation. These tasks are generally character-ized by a global team objective that is often temporally sparse - realized only upon completing an episode. The sparsity of the shared team objective often makes it an inadequate learning signal to learn effective strategies. Moreover, this reward signal does not capture the marginal contribution of each agent towards the global objective. This leads to the problem of structural credit assignment in multia-gent systems. Furthermore, due to a lack of accurate understanding of desired task behaviors, it is often challenging to manually design agent-specific rewards to improved coordination. While learning these undefined local objectives is very critical for a successful coordination, it is extremely challenging due to these two core challenges. Firstly, due to interaction among agents in an environment, the complexity of the problem may rise exponentially with the number of agents, and their behavioral sophisti-cation. An agent perceives the environment as non-stationary, due to all learn-ing concurrently. This leads to an agent perceiving the coordination objective as extremely noisy. Secondly, the goal information required to learn coordination behavior is distributed among agents. This makes it difficult for agents to learn undefined desired behaviors that optimizes a team objective. The key contribution of this work is to address the credit assignment problem in multiagent coordination using several semantically meaningful local rewards. We argue that real-world multiagent coordination tasks can be decomposed into several meaningful skills. Further, we introduce MADyS, a framework that can optimize a global reward by learning to dynamically select the most optimal skill from semantically meaningful skills, characterized by their local rewards, without requiring any form of reward shaping. Here, each local reward describes a basic skill and is designed based on domain knowledge. MADyS combines gradient-based optimization to maximize dense local rewards and gradient-free optimization to maximize the sparse team-based reward. Each local reward is used to train a local policy learner using policy gradient (PG) - and an evolutionary algorithm (EA) that searches in a population of policies to maximize the global objective by picking the most optimal local reward at each time step of an episode. While these two processes occur concurrently, the experiences collected by the EA population are stored in a replay buffer and utilized by the PG based local rewards optimizer for better sample efficiency. Our experimental results show that MADyS outperforms several baselines. We also visualize the complex coordination behaviors by studying the temporal distri-bution shifts of the selected local rewards. By visualizing these shifts throughout an episode, we gain insight into how agents learn to (i) decompose a complex task into various sub-tasks, (ii) dynamically configure sub-teams, and (iii) assign the selected sub-tasks to the sub-teams to optimize as a team on the global objective

Rank2Tell: A Multimodal Driving Dataset for Joint Importance Ranking and Reasoning

The widespread adoption of commercial autonomous vehicles (AVs) and advanced driver assistance systems (ADAS) may largely depend on their acceptance by society, for which their perceived trustworthiness and interpretability to riders are crucial. In general, this task is challenging because modern autonomous systems software relies heavily on black-box artificial intelligence models. Towards this goal, this paper introduces a novel dataset, Rank2Tell, a multi-modal ego-centric dataset for Ranking the importance level and Telling the reason for the importance. Using various close and open-ended visual question answering, the dataset provides dense annotations of various semantic, spatial, temporal, and relational attributes of various important objects in complex traffic scenarios. The dense annotations and unique attributes of the dataset make it a valuable resource for researchers working on visual scene understanding and related fields. Further, we introduce a joint model for joint importance level ranking and natural language captions generation to benchmark our dataset and demonstrate performance with quantitative evaluations