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Artemy Biryukov
Artemy Biryukov

6 Sensory Connections.mp4


While these components have been identified, their details have not been fully applied to artificial walking systems. For example, while animal experiments show that synaptic connections in sensory motor pathways are plastic (i.e., sensory adaptation) (Whelan and Pearson, 1997; Wolf and Büschges, 1997; Wark et al., 2007) to allow for stable locomotion and adaptation, this plasticity with continuous synaptic changes has been largely ignored in robotic implementation. Typically, the connections between sensory feedback and neural circuits for locomotion control of walking robots are static. To obtain stable locomotion, these connections are usually adjusted manually, or empirically chosen, for specific walking robots (Owaki et al., 2012; Barikhan et al., 2014). In some cases, machine learning techniques are employed first to optimize the connections through simulation before implementing them on real robots (Hwangbo et al., 2019). Accordingly, unexpected situations such as leg damage might lead to unstable locomotion if the sensory connection strength cannot be automatically or continuously adjusted to deliver proper information for adaptation. Furthermore, transferring the control system with the tuned or optimized connections from one walking robot to another might not work effectively.




6 sensory connections.mp4


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From this perspective, in this study, we introduce a fast learning mechanism for continuous online adaptation or flexible plasticity in sensory pathways (i.e., synaptic connection strength plasticity of sensory feedback) in order to (i) generate stable self-organized locomotion, (ii) deal with damage (known as lesion-induced plasticity), and (iii) be able to automatically adapt to different walking robots. Specifically, the learning mechanism will continuously adjust the connection strength between proprioceptive feedback (i.e., foot contact feedback) and distributed neural CPG-based control circuits (Figure 1). This approach combines bio-inspired key ingredients including: (1) distributed neural CPG-based control circuits without inter-circuit connections for flexible and independent individual leg control, (2) a learning mechanism for proprioceptive sensory adaptation, and (3) body-environment interaction, to acquire adaptive and flexible interlimb coordination for walking robots. This novel approach has more advantages compared to others (Ijspeert et al., 2007; Manoonpong et al., 2008, 2013; Inagaki et al., 2010; Asif, 2012; Ambe et al., 2013) in the following aspects:


where w1j,2j are the synaptic connection weights between the neurons, S1,2 are the CPG inputs (i.e., load sensing feedback), and oi are the CPG outputs. α is a sensory feedback connection (synaptic plasticity), automatically adjusted by dual rate learning (described in detail in the following section. See Equation 11). The CPG inputs are defined as:


The forward model implemented (Figure 3) predicts that sensory feedback should be zero while the leg is lifted (swing phase) and a high value when the leg is on the ground (stand phase). In other words, a positive sensor value is expected when the leg touches the ground during the stance phase (downward position of the CTr-joint) and a zero value while the leg is in the air during the swing phase (upward position of the CTr-joint). This simple strategy tries to make the robot follow a stable stroke potentially giving the robot body good propulsion along the whole stance phase of the step. The forward model is given as:


In this way, a proper feedback strength can be obtained after a few walking steps. Here, each adaptation process receives the same error and adapts the sensory feedback strength accordingly, as shown in the following equations:


Dual rate learning for the sensory feedback strength adaptation of CPG-based control. The sensory information from the foot contact sensor at each leg is transmitted to its corresponding CPG-based control. It shapes the CPG outputs to obtain a proper phase between legs. The learning process continuously adapts the strength of the sensory feedback to ensure that proper sensory information is transmitted to its corresponding CPG-based control. The forward model receives an efference copy (i.e., CTr motor command) and translates it into predicted foot contact sensory feedback which is then compared to the actual foot contact sensory feedback. The difference between them is sent to the learning process for sensory feedback strength adaptation.


Results for the four- and six-legged robots. For each configuration: (A) Scheme of the tested robot. (B,C) Sensory feedback strengths during simulation on the left front leg (l1) and right front leg (l2), respectively. A black line indicates the average value to which each strength converges. (D) The change in the robot walking speed during the simulation. (E) The swing and stance phases of each leg of the robot, defined by motor commands. Green areas represent stance phases, while white areas correspond to swing phases. Colored marks are used to visualize the formed gaits, namely a trot gait for four legs and a bipod gait for six legs. (F) Average walking speed from 200 tests. Videos showing examples of self-organized locomotion of the four- and six-legged robots can be viewed at: www.manoonpong.com/Frontiers2020/4legs.mp4 (or see Video S1) and www.manoonpong.com/Frontiers2020/6legs.mp4 (or see Video S2), respectively. Note that all sensory feedback strengths of the four- and six-legged robots are shown in Figures S1, S2.


Results for the 8- and 20-legged robots. For each configuration: (A) Scheme of the tested robot. (B,C) Sensory feedback strengths during simulation on the left front leg (l1) and right front leg (l2), respectively. A black line indicates the average value to which each strength converges. (D) The change in the robot walking speed during the simulation. (E) The swing and stance phases for each leg of the robot, defined by motor commands. Green areas represent stance phases, while white areas correspond to swing phases. Colored marks are used to visualize the formed gaits of the 8- and 20-legged robots. In this case, both robots show metachronal-like gaits. (F) Average walking speed from 200 tests. Videos showing examples of self-organized locomotion in the 8- and 20-legged robots can be viewed at: www.manoonpong.com/Frontiers2020/8legs.mp4 (or see Video S3) and www.manoonpong.com/Frontiers2020/20legs.mp4 (or see Video S4), respectively. Note that all sensory feedback strengths of the 8- and 20-legged robots are shown in Figures S3, S4.


Results for the four- and six-legged robots when disabling certain limbs after 15 s of simulation. For each configuration: (A) Scheme of the tested robot, including the limbs amputated (red cross) in this experiment. (B,C) Sensory feedback strengths during simulation on the left front leg (l1) and right front leg (l2), respectively. A line indicates the average value to which each strength converges. The convergence values before and after the amputation are drawn in black and red, respectively. (D) The change in the robot walking speed during the simulation. (E) The swing and stance phases of each leg of the robot, defined by motor commands. Green areas represent stance phases, while white areas correspond to swing phases. After amputation occurs, the state of the disabled limbs is represented in gray. Colored marks are used to visualize the formed gaits. (F) Average walking speed from 500 tests. For each simulation, a random number of arbitrary limbs was disabled, investigating the ability of the system to adapt to different morphologies. Videos showing examples of adaptation to leg damage of the four- and six-legged robots can be viewed at: www.manoonpong.com/Frontiers2020/4legsDamage.mp4 (or see Video S5) and www.manoonpong.com/Frontiers2020/6legsDamage.mp4 (or see Video S6), respectively. Note that all sensory feedback strengths of the four- and six-legged robots with leg damage are shown in Figures S5, S6.


Results for the 8- and 20-legged robots when disabling certain limbs after 15 s of simulation. For each configuration: (A) Scheme of the tested robot, including the limbs amputated (red cross) in this experiment. (B,C) Sensory feedback strengths during simulation on the left front leg (l1 in both 8- and 20-legged robots) and a right leg (l2 in the eight-legged robot and l4 in the 20-legged robot), respectively. A line indicates the average value to which each strength converges. The convergence values before and after the amputation are drawn in black and red, respectively. (D) The change in the robot walking speed during the simulation. (E) The swing and stance phases of each leg of the robot, defined by motor commands. Green areas represent stance phases, while white areas correspond to swing phases. After amputation occurs, the state of the disabled limbs is represented in gray. Colored marks are used to visualize the formed gaits. (F) Average walking speed from 500 tests. For each simulation, a random number of arbitrary limbs was disabled, investigating the ability of the system to adapt to different morphologies. Videos showing examples of adaptation to leg damage of the 8- and 20-legged robots can be viewed at: www.manoonpong.com/Frontiers2020/8legsDamage.mp4 (or see Video S7) and www.manoonpong.com/Frontiers2020/20legsDamage.mp4 (or see Video S8), respectively. Note that all sensory feedback strengths of the 8- and 20-legged robots with leg damage are shown in Figures S7, S8.


Average weights of the foot contact sensory feedback (i.e., sensory synaptic strength). Each li box represents the results for each i leg of the robots. Blue boxes represent the results for 200 runs on robots without any amputations. Gray boxes represent the results for 500 runs where randomized amputations were performed to obtain different configurations. In this case, the robots were amputated from the beginning of the simulation where the robots were still able to form gaits. (A) Results for the four-legged robot. (B) Results for the six-legged robot. (C) Results for the eight-legged robot. (D) Results for the 20-legged robot. 041b061a72


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