Decentralized control and local information for robust and adaptive decentralized Deep Reinforcement Learning.

The trend in industrial/service robotics is to develop robots that can cooperate with people, interacting with them in an autonomous, safe and purposive way. These are the fundamental elements characterizing the fourth and the fifth industrial revolutions (4IR, 5IR): the crucial innovation is the adoption of intelligent technologies that can allow the development of cyber-physical systems, similar if not superior to humans. The common wisdom is that intelligence might be provided by AI (Artificial Intelligence), a claim that is supported more by media coverage and commercial interests than by solid scientific evidence. AI is currently conceived in a quite broad sense, encompassing LLMs and a lot of other things, without any unifying principle, but self-motivating for the success in various areas. The current view of AI robotics mostly follows a purely disembodied approach that is consistent with the old-fashioned, Cartesian mind-body dualism, reflected in the software-hardware distinction inherent to the von Neumann computing architecture. The working hypothesis of this position paper is that the road to the next generation of autonomous robotic agents with cognitive capabilities requires a fully brain-inspired, embodied cognitive approach that avoids the trap of mind-body dualism and aims at the full integration of Bodyware and Cogniware. We name this approach Artificial Cognition (ACo) and ground it in Cognitive Neuroscience. It is specifically focused on proactive knowledge acquisition based on bidirectional human-robot interaction: the practical advantage is to enhance generalization and explainability. Moreover, we believe that a brain-inspired network of interactions is necessary for allowing humans to cooperate with artificial cognitive agents, building a growing level of personal trust and reciprocal accountability: this is clearly missing, although actively sought, in current AI. The ACo approach is a work in progress that can take advantage of a number of research threads, some of them antecedent the early attempts to define AI concepts and methods. In the rest of the paper we will consider some of the building blocks that need to be re-visited in a unitary framework: the principles of developmental robotics, the methods of action representation with prospection capabilities, and the crucial role of social interaction.

Artificial cognition vs. artificial intelligence for next-generation autonomous robotic agents

This article is a historical perspective on how the study of the neuromechanics of insects and other arthropods has inspired the construction, and especially the control, of hexapod robots. Many hexapod robots’ control systems share common features, including: 1. Direction of motor output of each joint (i.e. to flex or extend) in the leg is gated by an oscillatory or bistable gating mechanism; 2. The relative phasing between each joint is influenced by proprioceptive feedback from the periphery (e.g. joint angles, leg load) or central connections between joint controllers; and 3. Behavior can be directed (e.g. transition from walking along a straight path to walking along a curve) via low-dimensional, broadly-acting descending inputs to the network. These distributed control schemes are inspired by, and in some robots, closely mimic the organization of the nervous systems of insects, the natural hexapods, as well as crustaceans. Nearly a century of research has revealed organizational principles such as central pattern generators, the role of proprioceptive feedback in control, and command neurons. These concepts have inspired the control systems of hexapod robots in the past, in which these structures were applied to robot controllers with neuromorphic (i.e. distributed) organization, but not neuromorphic computational units (i.e. neurons) or computational hardware (i.e. hardware-accelerated neurons). Presently, several hexapod robots are controlled with neuromorphic computational units with or without neuromorphic organization, almost always without neuromorphic hardware. In the near future, we expect to see hexapod robots whose controllers include neuromorphic organization, computational units, and hardware. Such robots may exhibit the full mobility of their insect counterparts thanks to a ‘biology-first’ approach to controller design. This perspective article is not a comprehensive review of the neuroscientific literature but is meant to give those with engineering backgrounds a gentle introduction into the neuroscientific principles that underlie models and inspire neuromorphic robot controllers. A historical summary of hexapod robots whose control systems and behaviors use neuromorphic elements is provided. Robots whose controllers closely model animals and may be used to generate concrete hypotheses for future animal experiments are of particular interest to the authors. The authors hope that by highlighting the decades of experimental research that has led to today’s accepted organization principles of arthropod nervous systems, engineers may better understand these systems and more fully apply biological details in their robots. To assist the interested reader, deeper reviews of particular topics from biology are suggested throughout.

https://iopscience.iop.org/article/10.1088/2634-4386/acc04f/pdf

A perspective on the neuromorphic control of legged locomotion in past, present, and future insect-like robots

Decentralized control has been established as a key control principle in insect walking and has been successfully leveraged to account for a wide range of walking behaviors in the proposed neuroWalknet architecture. This controller allows for walking patterns with different velocities in forward and backward direction — quite similar to the behavior shown in stick insects —, for negotiation of curves, and for robustly dealing with various disturbances. While these simulations focus on the cooperation of different, decentrally controlled legs, here we consider a set of biological experiments not yet been tested by neuroWalknet, that focus on the function of the individual leg and are context dependent. These intraleg studies deal with four groups of interjoint reflexes. The reflexes are elicited by either a stimulation of the femoral chordotonal organ (fCO) or a specific stimulation of campaniform sensilla (CS). Motor output signals are recorded from the alpha-joint, the beta-joint or the gamma-joint of the leg. Furthermore, such reflexes have been studied while the ganglion was treated with pilocarpine. Although these biological data represent results obtained from different local reflexes in different contexts, they fit with and are embedded into the behavior shown by the global structure of neuroWalknet. In particular, a specific and intensively studied behavior, active reaction, has since long been assumed to represent a separate behavioral element, from which it is not clear why it occurs in some situations, but not in others. This question could now be explained as an emergent property of the holistic structure of neuroWalknet. When experimenting with pilocarpine, oscillations were induced in neuroWalknet even though this does not include an explicit central pattern generator and in this way provides a simpler model as a functional explanation. As the simulation data result from a holistic system, further results were obtained that could be used as predictions to be tested in further biological experiments. AUTHOR SUMMARY Behavior of animals can be studied by detailed observation, but observation alone does not explain the function of the underlying neuronal controller structures. To better understand this function, an important tool can be to develop an artificial structure based on simulated neurons and a simulated or physical body. Although typical animal behavior appears complex, the corresponding neuronal structures may be comparatively simple. The goal for such a hypothetical structure should be to include as many different behaviors as possible, and, at the same time, search for a simple explanation consisting of a minimum of neuronal elements. Furthermore, such a simulation system, e.g. an artificial neuronal network, should contain hypotheses that can be tested in biological experiments. We propose an extension to such a network that is based on a decentralized neuronal structure, using a neural network as a scaffold, that enables various combinations of local neuronal elements that allow for emergent, i.e. not explicitly designed properties. Indeed, neuroWalknet contains further abilities not yet recognized in the earlier version. For instance, neither explicit structures like central pattern generators nor explicit Active Reaction are required to reproduce typical intraleg reactions. Therefore, neuroWalknet presents a holistic approach enabling emergent properties out of the cooperation of small neuronal elements that are context dependent instead of explicit, dedicated elements.

/pdf/neurowalknet-a-controller-for-hexapod-walking-allowing-for-3hvtoyzd.pdf

neuroWalknet, a controller for hexapod walking allowing for context dependent behavior

Curve walking is an important skill for multi-legged robot locomotion as it increases a robots’ maneuverability. We investigate whether a decentralized system can explain even complex walking behaviors like curve walking. Based on an analysis of the curve walking capabilities of the decentralized control architecture Walknet, we propose a couple of simple but effective modifications with a main focus on the coordination between leg controllers: controlling the step length, shifting the AEP (transition point from swing to stance), and decreasing the step length for the legs on the inside of the curve. In simulation, the modified architecture demonstrated a significant improvement in the stability of the curve walking performance for tight curves and even allows a smooth transition to extremely tight curves and turning on the spot. Furthermore, the system is tested on a real robot and showed good qualitative results and robust curve walking behavior. 

Adaptation of a Decentralized Controller to Curve Walking in a Hexapod Robot

Locomotion in animals provides a model for adaptive behavior as it is able to deal with various kinds of perturbations. Work in insects suggests that this evolved flexibility results from a modular architecture, which can be characterized by a recurrent neural network allowing for various emerging attractor states. Whereas a lower control-level coordinates joint movements on a short timescale, a higher-level handles action selection on longer timescales. Implementation of such a control system on a walking hexapod robot was able to deal with various walking patterns including disturbances such as uneven terrain or loss of a leg. Here, we propose a cognitive expansion to the adaptive control system that allows dealing with novel challenging situations. This approach makes use of an internal simulation-based planner that is triggered when the model-free controller fails to recover from an unstable pose. Using a grounded internal body model, the planner then tries, in internal simulation, different solutions out of context, and thus, proposes a new plan to be executed on the real robot. We demonstrate the feasibility of this control approach for walking over terrain with uncertain footholds in three scenarios.

/pdf/from-adaptive-locomotion-to-predictive-action-selection-l646ltx6ji.pdf

From Adaptive Locomotion to Predictive Action Selection – Cognitive Control for a Six-Legged Walker

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From Adaptive Locomotion to Predictive Action Selection – Cognitive Control for a Six-Legged Walker