There have been widespread claims about Large Language Models (LLMs) being able to successfully verify or self-critique their candidate solutions in reasoning problems in an iterative mode. Intrigued by those claims, in this paper we set out to investigate the verification/self-critiquing abilities of large language models in the context of planning. We evaluate a planning system that employs LLMs for both plan generation and verification. We assess the verifier LLM's performance against ground-truth verification, the impact of self-critiquing on plan generation, and the influence of varying feedback levels on system performance. Using GPT-4, a state-of-the-art LLM, for both generation and verification, our findings reveal that self-critiquing appears to diminish plan generation performance, especially when compared to systems with external, sound verifiers and the LLM verifiers in that system produce a notable number of false positives, compromising the system's reliability. Additionally, the nature of feedback, whether binary or detailed, showed minimal impact on plan generation.

Lee et al. (2020) utilize relative-attribute information in robot skill learning, but their GAN-based formulation is restricted to static visual attributes and is not applicable to temporally-extended concepts. This paper adopts a similar setup to works that learn diverse skills or motion styles from largescale offline behavior datasets or demonstrations (Lee & Popović, 2010; Wang et al., 2017; Zhou & Dragan, 2018; Peng et al., 2018b; Luo et al., 2020; Chebotar et al., 2021; Peng et al., 2021). These works emphasize on modeling a variety of reusable motor skills by learning a low-level controller conditioned on skill latent codes. Since the latent codes are inscrutable to humans, for each new task, the user must specify the desirable agent behavior by constructing an engineered symbolic reward and use it to train a separate high-level policy that controls the low-level controller. Our methods are complemented by existing diverse-skill learning methods because skill priors (i.e., pre-trained low-level controllers) allow us to optimize the behavioral reward more efficiently. More recently, there have been works in diffusion-based text-to-motion animation generation (Tevet et al., 2022; Guo et al., 2022). They are similar to this work in the sense that we both allow humans to control the agent behavior through explicit concepts. However, they do not support fine-grained control over the strength of individual behavioral attributes, and their works are not applicable to physics-based character control.

Intrigued by the claims of emergent reasoning capabilities in LLMs trained on general web corpora, in this paper, we set out to investigate their planning capabilities. We aim to evaluate (1) how good LLMs are by themselves in generating and validating simple plans in commonsense planning tasks (of the type that humans are generally quite good at) and (2) how good LLMs are in being a source of heuristic guidance for other agents--either AI planners or human planners--in their planning tasks. To investigate these questions in a systematic rather than anecdotal manner, we start by developing a benchmark suite based on the kinds of domains employed in the International Planning Competition. On this benchmark, we evaluate LLMs in three modes: autonomous, heuristic and human-in-the-loop. Our results show that LLM's ability to autonomously generate executable plans is quite meager, averaging only about 3% success rate. The heuristic and human-in-the-loop modes show slightly more promise. In addition to these results, we also make our benchmark and evaluation tools available to support investigations by research community.

Empowering decision support systems with automated planning has received significant recognition in the planning community. The central idea for such systems is to augment the capabilities of the human-in-the-loop with automated planning techniques and provide timely support to enhance the decision-making experience. In addition to this, an effective decision support system must be able to provide intuitive explanations based on specific queries on proposed decisions to its end users. This makes decision-support systems an ideal test-bed to study the effectiveness of various XAIP techniques being developed in the community. To this end, we present our decision support system RADAR-X that extends RADAR (Grover et al. 2020) by allowing the user to participate in an interactive explanatory dialogue with the system. Specifically, we allow the user to ask for contrastive explanations, wherein the user can try to understand why a specific plan was chosen over an alternative (referred to as the foil). Furthermore, we use the foil raised as evidence for unspecified user preferences and use it to further refine plan suggestions.