237 research outputs found
Legged locomotion over irregular terrains: State of the art of human and robot performance
Legged robotic technologies have moved out of the lab to operate in real environments, characterized by a wide variety of unpredictable irregularities and disturbances, all this in close proximity with humans. Demonstrating the ability of current robots to move robustly and reliably in these conditions is becoming essential to prove their safe operation. Here, we report an in-depth literature review aimed at verifying the existence of common or agreed protocols and metrics to test the performance of legged system in realistic environments. We primarily focused on three types of robotic technologies, i.e., hexapods, quadrupeds and bipeds. We also included a comprehensive overview on human locomotion studies, being it often considered the gold standard for performance, and one of the most important sources of bioinspiration for legged machines. We discovered that very few papers have rigorously studied robotic locomotion under irregular terrain conditions. On the contrary, numerous studies have addressed this problem on human gait, being nonetheless of highly heterogeneous nature in terms of experimental design. This lack of agreed methodology makes it challenging for the community to properly assess, compare and predict the performance of existing legged systems in real environments. On the one hand, this work provides a library of methods, metrics and experimental protocols, with a critical analysis on the limitations of the current approaches and future promising directions. On the other hand, it demonstrates the existence of an important lack of benchmarks in the literature, and the possibility of bridging different disciplines, e.g., the human and robotic, towards the definition of standardized procedure that will boost not only the scientific development of better bioinspired solutions, but also their market uptake
Adaptive Force-Based Control of Dynamic Legged Locomotion over Uneven Terrain
Agile-legged robots have proven to be highly effective in navigating and
performing tasks in complex and challenging environments, including disaster
zones and industrial settings. However, these applications normally require the
capability of carrying heavy loads while maintaining dynamic motion. Therefore,
this paper presents a novel methodology for incorporating adaptive control into
a force-based control system. Recent advancements in the control of quadruped
robots show that force control can effectively realize dynamic locomotion over
rough terrain. By integrating adaptive control into the force-based controller,
our proposed approach can maintain the advantages of the baseline framework
while adapting to significant model uncertainties and unknown terrain impact
models. Experimental validation was successfully conducted on the Unitree A1
robot. With our approach, the robot can carry heavy loads (up to 50% of its
weight) while performing dynamic gaits such as fast trotting and bounding
across uneven terrains
SYNLOCO: Synthesizing Central Pattern Generator and Reinforcement Learning for Quadruped Locomotion
The Central Pattern Generator (CPG) is adept at generating rhythmic gait
patterns characterized by consistent timing and adequate foot clearance. Yet,
its open-loop configuration often compromises the system's control performance
in response to environmental variations. On the other hand, Reinforcement
Learning (RL), celebrated for its model-free properties, has gained significant
traction in robotics due to its inherent adaptability and robustness. However,
initiating traditional RL approaches from the ground up presents computational
challenges and a heightened risk of converging to suboptimal local minima. In
this paper, we propose an innovative quadruped locomotion framework, SYNLOCO,
by synthesizing CPG and RL that can ingeniously integrate the strengths of both
methods, enabling the development of a locomotion controller that is both
stable and natural. Furthermore, we introduce a set of performance-driven
reward metrics that augment the learning of locomotion control. To optimize the
learning trajectory of SYNLOCO, a two-phased training strategy is presented.
Our empirical evaluation, conducted on a Unitree GO1 robot under varied
conditions--including distinct velocities, terrains, and payload
capacities--showcases SYNLOCO's ability to produce consistent and clear-footed
gaits across diverse scenarios. The developed controller exhibits resilience
against substantial parameter variations, underscoring its potential for robust
real-world applications.Comment: 7 Page
Proprioception and Tail Control Enable Extreme Terrain Traversal by Quadruped Robots
Legged robots leverage ground contacts and the reaction forces they provide
to achieve agile locomotion. However, uncertainty coupled with contact
discontinuities can lead to failure, especially in real-world environments with
unexpected height variations such as rocky hills or curbs. To enable dynamic
traversal of extreme terrain, this work introduces 1) a proprioception-based
gait planner for estimating unknown hybrid events due to elevation changes and
responding by modifying contact schedules and planned footholds online, and 2)
a two-degree-of-freedom tail for improving contact-independent control and a
corresponding decoupled control scheme for better versatility and efficiency.
Simulation results show that the gait planner significantly improves stability
under unforeseen terrain height changes compared to methods that assume fixed
contact schedules and footholds. Further, tests have shown that the tail is
particularly effective at maintaining stability when encountering a terrain
change with an initial angular disturbance. The results show that these
approaches work synergistically to stabilize locomotion with elevation changes
up to 1.5 times the leg length and tilted initial states.Comment: 8 pages, 9 figures, accepted to IEEE/RSJ International Conference on
Intelligent Robots and Systems (IROS) 202
Learning Agility and Adaptive Legged Locomotion via Curricular Hindsight Reinforcement Learning
Agile and adaptive maneuvers such as fall recovery, high-speed turning, and
sprinting in the wild are challenging for legged systems. We propose a
Curricular Hindsight Reinforcement Learning (CHRL) that learns an end-to-end
tracking controller that achieves powerful agility and adaptation for the
legged robot. The two key components are (I) a novel automatic curriculum
strategy on task difficulty and (ii) a Hindsight Experience Replay strategy
adapted to legged locomotion tasks. We demonstrated successful agile and
adaptive locomotion on a real quadruped robot that performed fall recovery
autonomously, coherent trotting, sustained outdoor speeds up to 3.45 m/s, and
tuning speeds up to 3.2 rad/s. This system produces adaptive behaviours
responding to changing situations and unexpected disturbances on natural
terrains like grass and dirt
ON TRAVERSABILITY COST EVALUATION FROM PROPRIOCEPTIVE SENSING FOR A CRAWLING ROBOT
Traversability characteristics of the robot working environment are crucial in planning an efficient path for a robot operating in rough unstructured areas. In the literature, approaches to wheeled or tracked robots can be found, but a relatively little attention is given to walking multi-legged robots. Moreover, the existing approaches for terrain traversability assessment seem to be focused on gathering key features from a terrain model acquired from range data or camera image and only occasionally supplemented with proprioceptive sensing that expresses the interaction of the robot with the terrain. This paper addresses the problem of traversability cost evaluation based on proprioceptive sensing for a hexapod walking robot while optimizing different criteria. We present several methods of evaluating the robot-terrain interaction that can be used as a cost function for an assessment of the robot motion that can be utilized in high-level path-planning algorithms
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