Chain-of-thought (CoT) prompting is a popular in-context learning (ICL) approach for large language models (LLMs), especially when tackling complex reasoning tasks. Traditional ICL approaches construct prompts using examples that contain questions similar to the input question. However, CoT prompting, which includes crucial intermediate reasoning steps (rationales) within its examples, necessitates selecting examples based on these rationales rather than the questions themselves. Existing methods require human experts or pre-trained LLMs to describe the skill, a high-level abstraction of rationales, to guide the selection. These methods, however, are often costly and difficult to scale. Instead, this paper introduces a new approach named Latent Reasoning Skills (LaRS) that employs unsupervised learning to create a latent space representation of rationales, with a latent variable called a reasoning skill. Concurrently, LaRS learns a reasoning policy to determine the required reasoning skill for a given question. Then the ICL examples are selected by aligning the reasoning skills between past examples and the question. This approach is theoretically grounded and compute-efficient, eliminating the need for auxiliary LLM inference or manual prompt design. Empirical results demonstrate that LaRS consistently outperforms SOTA skill-based selection methods, processing example banks four times faster, reducing LLM inferences during the selection stage by half, and showing greater robustness to sub-optimal example banks.

LaRS: Latent Reasoning Skills for Chain-of-Thought Reasoning

This work introduces a robotics platform which comprehensively integrates multi-step action planning, natural language understanding, and memory to interactively perform service tasks in accordance with variable needs and intentions of users. The proposed architecture is built around an AI agent, derived from GPT-4, which has been embedded in an embodied system. Our approach utilizes semantic matching, plan validation, and state messages to ground the agent in the physical world, enabling a seamless merger between communication and behavior. We demonstrated the advantages of this system with an HRI study comparing mobile robots with and without conversational AI capabilities in a free-form tour-guide scenario. The increased adaptability of the system is measured along five dimensions: flexible tasking planning, interactive exploration of information, emotional friendliness, personalization, and increased overall user satisfaction.

Dobby: A Conversational Service Robot Driven by GPT-4

Robustly cooperating with unseen agents and human partners presents significant challenges due to the diverse cooperative conventions these partners may adopt. Existing Ad Hoc Teamwork (AHT) methods address this challenge by training an agent with a population of diverse teammate policies obtained through maximizing specific diversity metrics. However, prior heuristic-based diversity metrics do not always maximize the agent's robustness in all cooperative problems. In this work, we first propose that maximizing an AHT agent's robustness requires it to emulate policies in the minimum coverage set (MCS), the set of best-response policies to any partner policies in the environment. We then introduce the L-BRDiv algorithm that generates a set of teammate policies that, when used for AHT training, encourage agents to emulate policies from the MCS. L-BRDiv works by solving a constrained optimization problem to jointly train teammate policies for AHT training and approximating AHT agent policies that are members of the MCS. We empirically demonstrate that L-BRDiv produces more robust AHT agents than state-of-the-art methods in a broader range of two-player cooperative problems without the need for extensive hyperparameter tuning for its objectives.  Our study shows that L-BRDiv outperforms the baseline methods by prioritizing discovering distinct members of the MCS instead of repeatedly finding redundant policies.

Minimum Coverage Sets for Training Robust Ad Hoc Teamwork Agents | VIDEO

We consider algorithms for learning reward functions from human preferences over pairs of trajectory segments, as used in reinforcement learning from human feedback (RLHF). Most recent work assumes that human preferences are generated based only upon the reward accrued within those segments, or their \textit{partial return}. Recent work casts doubt on the validity of this assumption, proposing an alternative preference model based upon \textit{regret}. We investigate the consequences of assuming preferences are based upon partial return when they actually arise from regret. We argue that the learned function is an approximation of the optimal advantage function, $\widehat{A^*_r}$, \textit{not} a reward function. We find that if a specific pitfall is addressed, this incorrect assumption is not particularly harmful, resulting in a highly shaped reward function. Nonetheless, this incorrect usage of $\widehat{A^*_r}$ is less desirable than the appropriate and simpler approach of greedy maximization of $\widehat{A^*_r}$. From the perspective of the regret preference model, we also provide a clearer interpretation of fine tuning contemporary large language models with RLHF. This paper overall provides insight regarding why learning under the partial return preference model tends to work so well in practice, despite it conforming poorly to how humans give preferences.

Learning Optimal Advantage from Preferences and Mistaking It for Reward

Current approaches to multi-agent cooperation rely heavily on centralized mechanisms or explicit communication protocols to ensure convergence. This paper studies the problem of distributed multi-agent learning without resorting to centralized components or explicit communication. It examines the use of distribution matching to facilitate the coordination of independent agents. In the proposed scheme, each agent independently minimizes the distribution mismatch to the corresponding component of a target visitation distribution. The theoretical  analysis shows that under certain conditions, each agent minimizing its individual  distribution mismatch allows the convergence to the joint policy that generated the target distribution. Further, if the target distribution is from a joint policy that optimizes a cooperative task, the optimal policy for a combination of this task reward and the distribution matching reward is the same joint policy. This insight is used to formulate a practical algorithm (\textsc{dm$^2$}), in which each individual agent matches a target distribution derived from concurrently sampled trajectories from a joint expert policy. Experimental validation on the StarCraft domain shows that combining (1) a task reward, and (2) a distribution matching reward for expert demonstrations for the same task, allows agents to outperform a naive distributed baseline. Additional experiments probe the conditions under which expert demonstrations need to be sampled to obtain the learning benefits.

DM$^2$: Decentralized Multi-Agent Reinforcement Learning via Distribution Matching

Goal-conditioned reinforcement learning (GCRL) has a wide range of potential real-world applications, including manipulation and navigation problems in robotics. Especially in such robotics tasks, sample efficiency is of the utmost importance for GCRL since, by default, the agent is only rewarded when it reaches its goal. While several methods have been proposed to improve the sample efficiency of GCRL, one relatively under-studied approach is the design of neural architectures to support sample efficiency.
In this work, we introduce a novel neural architecture for GCRL that achieves significantly better sample efficiency than the commonly-used monolithic network architecture. 
The key insight is that the 
optimal action-value function $Q^*(s, a, g)$ must satisfy the triangle inequality in a specific sense.
Furthermore, we introduce the \emph{metric residual network} (MRN)  that deliberately decomposes the action-value function $Q(s,a,g)$ into the negated summation of a metric plus a residual asymmetric component. MRN provably approximates any optimal action-value function $Q^*(s,a,g)$, thus making it a fitting neural architecture for GCRL.
We conduct comprehensive experiments across 12 standard benchmark environments in GCRL. The empirical results demonstrate that MRN uniformly outperforms other state-of-the-art GCRL neural architectures in terms of sample efficiency. The code is publicly available at \url{https://github.com/Cranial-XIX/metric-residual-network}.

Metric Residual Network for Sample Efficient Goal-conditioned Reinforcement Learning

In reinforcement learning (RL), a reward function that aligns exactly with a task's true performance metric is often necessarily sparse. For example, a true task metric might encode a reward of 1 upon success and 0 otherwise. The sparsity of these true task metrics can make them hard to learn from, so in practice they are often replaced with alternative dense reward functions. These dense reward functions are typically designed by experts through an ad hoc process of trial and error. In this process, experts manually search for a reward function that improves performance with respect to the task metric while also enabling an RL algorithm to learn faster. This process raises the question of whether the same reward function is optimal for all algorithms, i.e., whether the reward function can be overfit to a particular algorithm. In this paper, we study the consequences of this wide yet unexamined practice of trial-and-error reward design. We first conduct computational experiments that confirm that reward functions can be overfit to learning algorithms and their hyperparameters. We then conduct a controlled observation study which emulates expert practitioners' typical experiences of reward design, in which we similarly find evidence of reward function overfitting. We also find that experts' typical approach to reward design---of adopting a myopic strategy and weighing the relative goodness of each state-action pair---leads to misdesign through invalid task specifications, since RL algorithms use cumulative reward rather than rewards for individual state-action pairs as an optimization target.

Code and supporting data for all experiments: https://github.com/serenabooth/reward-design-perils.

The Perils of Trial-and-Error Reward Design: Misdesign through Overfitting and Invalid Task Specifications

Automated care systems are becoming more tangible than ever: recent breakthroughs in robotics and machine learning can be used to address the need for automated care created by the increasing aging population. However, such systems require overcoming several technological, ethical, and social challenges. One inspirational manifestation of these challenges can be observed in the training of seeing-eye dogs for visually impaired people. A seeing-eye dog is not just trained to obey its owner, but also to "intelligently disobey": if it is given an unsafe command from its handler, it is taught to disobey it or even insist on a different course of action. This paper proposes the challenge of building a seeing-eye robot, as a thought-provoking use-case that helps identify the challenges to be faced when creating behaviors for robot assistants in general. Through this challenge, this paper delineates the prerequisites that an automated care system will need to have in order to perform intelligent disobedience and to serve as a true agent for its handler.

The Seeing-Eye Robot Grand Challenge: Rethinking Automated Care (Blue Sky Ideas Track)

A desirable goal for autonomous agents is to be able to coordinate on the fly with previously unknown teammates.  Known as â€œad hoc teamworkâ€, enabling such a capability has been receiving increasing attention in the research community. One of the central challenges in ad hoc teamwork is quickly recognizing the current plans of other agents and planning accordingly.  In this paper, we focus on the scenario in which teammates can communicate with one another, but only at a cost.  Thus, they must carefully balance plan recognition based on observations vs. that based on communication.
    
    This paper proposes a new metric for evaluating how similar are two policies that a teammate may be following - the Expected Divergence Point (EDP).
    We then present a novel planning algorithm for ad hoc teamwork, determining which query to ask and planning accordingly. We demonstrate the effectiveness of this algorithm in a range of increasingly general communication in ad hoc teamwork problems.

Expected Value of Communication for Planning in Ad Hoc Teamwork

Human-robot shared autonomy techniques for vehicle navigation hold promise for reducing a human driverâ€™s workload, ensuring safety, and improving navigation efficiency. However, because typical techniques achieve these improvements by effectively removing human control at critical moments, these approaches often exhibit poor responsiveness to human commandsâ€”especially in cluttered environments. In this paper, we propose a novel goal-blending shared autonomy (GBSA) system, which aims to improve responsiveness in shared autonomy systems by blending human and robot input during the selection of local navigation goals as opposed to low-level motor (servo-level) commands. We validate the proposed approach by performing a human study involving an intelligent wheelchair and compare GBSA to a representative servo-level shared control system that uses a policy-blending approach. The results of both quantitative performance analysis and a subjective survey show that GBSA exhibits significantly better system responsiveness and induces higher user satisfaction than the existing approach.

Goal Blending for Responsive Shared Autonomy in a Navigating Vehicle

In continuing tasks, average-reward reinforcement learning may be a more appropriate problem formulation than the more common discounted reward formulation. As usual, learning an optimal policy in this setting typically requires a large amount of training experiences. Reward shaping is a common approach for incorporating domain knowledge into reinforcement learning in order to speed up convergence to an optimal policy. However, to the best of our knowledge, the theoretical properties of reward shaping have thus far only been established in the discounted setting. This paper presents the first reward shaping framework for average-reward learning and proves that, under standard assumptions, the optimal policy under the original reward function can be recovered. In order to avoid the need for manual construction of the shaping function, we introduce a method for utilizing domain knowledge expressed as a temporal logic formula. The formula is automatically translated to a shaping function that provides additional reward throughout the learning process. We evaluate the proposed method on three continuing tasks. In all cases, shaping speeds up the average-reward learning rate without any reduction in the performance of the learned policy compared to relevant baselines.

Temporal-Logic-Based Reward Shaping for Continuing Reinforcement Learning Tasks

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Dear Colleagues and Friends,

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The 33rd IEEE International Conference on Robot and Human Interactive Communication was held in Pasadena, California, USA. The theme of the IEEE RO-MAN 2024 was “Embracing Human-Centered HRI”.

Main Track - Multi Agent Systems

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ML: Reinforcement Learning Algorithms 3

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Presentations

LaRS: Latent Reasoning Skills for Chain-of-Thought Reasoning

Dobby: A Conversational Service Robot Driven by GPT-4

Minimum Coverage Sets for Training Robust Ad Hoc Teamwork Agents | VIDEO

Learning Optimal Advantage from Preferences and Mistaking It for Reward

DM$^2$: Decentralized Multi-Agent Reinforcement Learning via Distribution Matching

Metric Residual Network for Sample Efficient Goal-conditioned Reinforcement Learning

The Perils of Trial-and-Error Reward Design: Misdesign through Overfitting and Invalid Task Specifications

The Seeing-Eye Robot Grand Challenge: Rethinking Automated Care (Blue Sky Ideas Track)

Expected Value of Communication for Planning in Ad Hoc Teamwork

Goal Blending for Responsive Shared Autonomy in a Navigating Vehicle

Temporal-Logic-Based Reward Shaping for Continuing Reinforcement Learning Tasks

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