Large language models (LLMs) have demonstrated exciting progress in acquiring
diverse new capabilities through in-context learning, ranging from logical
reasoning to code-writing. Robotics researchers have also explored using LLMs
to advance the capabilities of robotic control. However, since low-level robot
actions are hardware-dependent and underrepresented in LLM training corpora,
existing efforts in applying LLMs to robotics have largely treated LLMs as
semantic planners or relied on human-engineered control primitives to interface
with the robot. On the other hand, reward functions are shown to be flexible
representations that can be optimized for control policies to achieve diverse
tasks, while their semantic richness makes them suitable to be specified by
LLMs. In this work, we introduce a new paradigm that harnesses this realization
by utilizing LLMs to define reward parameters that can be optimized and
accomplish variety of robotic tasks. Using reward as the intermediate interface
generated by LLMs, we can effectively bridge the gap between high-level
language instructions or corrections to low-level robot actions. Meanwhile,
combining this with a real-time optimizer, MuJoCo MPC, empowers an interactive
behavior creation experience where users can immediately observe the results
and provide feedback to the system. To systematically evaluate the performance
of our proposed method, we designed a total of 17 tasks for a simulated
quadruped robot and a dexterous manipulator robot. We demonstrate that our
proposed method reliably tackles 90% of the designed tasks, while a baseline
using primitive skills as the interface with Code-as-policies achieves 50% of
the tasks. We further validated our method on a real robot arm where complex
manipulation skills such as non-prehensile pushing emerge through our
interactive system.

本文提出了一种新颖的方法，即通过利用大型语言模型 (LLMs) 定义奖励参数，从而优化和实现各种机器人任务，并结合实时优化器 MuJoCo MPC，使用户可以交互地创造行为并快速获得结果反馈。作者在一个模拟仿真机器人四足动物和一种机械手进行了 17 个任务的评价，证明了这种方法成功解决了 90% 的任务，并在真实机器人手臂上验证了方法的有效性，包括非握持推动等复杂操作技能。

机器技能综合的语言到奖励转换

Language to Rewards for Robotic Skill Synthesis

Behavior prediction plays an important role in integrated autonomous driving
software solutions. In behavior prediction research, interactive behavior
prediction is a less-explored area, compared to single-agent behavior
prediction. Predicting the motion of interactive agents requires initiating
novel mechanisms to capture the joint behaviors of the interactive pairs. In
this work, we formulate the end-to-end joint prediction problem as a sequential
learning process of marginal learning and joint learning of vehicle behaviors.
We propose ProspectNet, a joint learning block that adopts the weighted
attention score to model the mutual influence between interactive agent pairs.
The joint learning block first weighs the multi-modal predicted candidate
trajectories, then updates the ego-agent's embedding via cross attention.
Furthermore, we broadcast the individual future predictions for each
interactive agent into a pair-wise scoring module to select the top $K$
prediction pairs. We show that ProspectNet outperforms the Cartesian product of
two marginal predictions, and achieves comparable performance on the Waymo
Interactive Motion Prediction benchmarks.

本文提出了 ProspectNet 作为一种新的机制，通过采用加权注意分数来建模交互代理对之间的相互影响和联合行为识别来解决自主驾驶软件解决方案中的行为预测问题。实验结果表明 ProspectNet 性能优于两个边缘预测的 Cartesian 乘积，并在 Waymo 交互运动预测基准上达到了可比较的性能。