Learning a Unified Control Policy for Safe Falling

Being able to fall safely is a necessary motor skill for humanoids performing highly dynamic tasks, such as running and jumping. We propose a new method to learn a policy that minimizes the maximal impulse during the fall. The optimization solves for both a discrete contact planning problem and a continuous optimal control problem. Once trained, the policy can compute the optimal next contacting body part (e.g. left foot, right foot, or hands), contact location and timing, and the required joint actuation. We represent the policy as a mixture of actor-critic neural network, which consists of n control policies and the corresponding value functions. Each pair of actor-critic is associated with one of the n possible contacting body parts. During execution, the policy corresponding to the highest value function will be executed while the associated body part will be the next contact with the ground. With this mixture of actor-critic architecture, the discrete contact sequence planning is solved through the selection of the best critics while the continuous control problem is solved by the optimization of actors. We show that our policy can achieve comparable, sometimes even higher, rewards than a recursive search of the action space using dynamic programming, while enjoying 50 to 400 times of speed gain during online execution

Words into Action: Learning Diverse Humanoid Robot Behaviors using Language Guided Iterative Motion Refinement

Humanoid robots are well suited for human habitats due to their morphological similarity, but developing controllers for them is a challenging task that involves multiple sub-problems, such as control, planning and perception. In this paper, we introduce a method to simplify controller design by enabling users to train and fine-tune robot control policies using natural language commands. We first learn a neural network policy that generates behaviors given a natural language command, such as "walk forward", by combining Large Language Models (LLMs), motion retargeting, and motion imitation. Based on the synthesized motion, we iteratively fine-tune by updating the text prompt and querying LLMs to find the best checkpoint associated with the closest motion in history. We validate our approach using a simulated Digit humanoid robot and demonstrate learning of diverse motions, such as walking, hopping, and kicking, without the burden of complex reward engineering. In addition, we show that our iterative refinement enables us to learn 3x times faster than a naive formulation that learns from scratch

Magnetic susceptibility of alkali-TCNQ salts and extended Hubbard models with bond order and charge density wave phases

The molar spin susceptibilities $\chi(T)$ of Na-TCNQ, K-TCNQ and Rb-TCNQ(II) are fit quantitatively to 450 K in terms of half-filled bands of three one-dimensional Hubbard models with extended interactions using exact results for finite systems. All three models have bond order wave (BOW) and charge density wave (CDW) phases with boundary $V = V_c(U)$ for nearest-neighbor interaction $V$ and on-site repulsion $U$. At high $T$, all three salts have regular stacks of $\rm TCNQ^-$ anion radicals. The $\chi(T)$ fits place Na and K in the CDW phase and Rb(II) in the BOW phase with $V \approx V_c$. The Na and K salts have dimerized stacks at $T < T_d$ while Rb(II) has regular stacks at 100K. The $\chi(T)$ analysis extends to dimerized stacks and to dimerization fluctuations in Rb(II). The three models yield consistent values of $U$, $V$ and transfer integrals $t$ for closely related $\rm TCNQ^-$ stacks. Model parameters based on $\chi(T)$ are smaller than those from optical data that in turn are considerably reduced by electronic polarization from quantum chemical calculation of $U$, $V$ and $t$ on adjacent $\rm TCNQ^-$ ions. The $\chi(T)$ analysis shows that fully relaxed states have reduced model parameters compared to optical or vibration spectra of dimerized or regular $\rm TCNQ^-$ stacks.Comment: 9 pages and 5 figure

Karyotype and nucleic acid content in Zantedeschia aethiopica Spr. and Zantedeschia elliottiana Engl.

Analysis of karyotype, nucleic deoxyribonucleic acid (DNA) content and sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) were performed in Zantedeschia aethiopica and Zantedeschia elliottiana. Mitotic metaphase in both species showed 2n=32. The chromosomes of both species were quite similar with medium length ranging from 1.55 ± 0.04 to 3.85 ± 0.12 μM in Z. aethiopica and 2.15 ± 0.04 to 3.90 ± 0.12 μM in Z. elliottiana. However, some differences were found in morphology and centromeric position among the chromosomes. Identification of individual chromosomes was carried out using chromosomes length, and centromeric positions. The karyotype of Z. aethiopica was determined to be 2n = 32 = 14 m + 18 sm and of Z. elliottiana to be 2n = 32 = 10 m + 22 sm. The 2C nuclear DNA content was found to be 3.72 ± 0.10 picograms (equivalent to 3638.16 mega base pairs) for Z. aethiopica and 1144.26 ± 0.05 picograms (equivalent to 1144.26 mega base pairs) for Z. elliottiana. Leaf protein analysis showed 11 and 9 bands for Z. aethiopica and Z. elliottiana, respectively, among which some were species specific. These results may provide useful information regarding Zantedeschia for the study of taxonomic relationships, genetics and breeding.Keywords: Zantedeschia, karyotype, mitotic metaphase, chromosomes, flow cytometr