A method performed by a processor of a system for training a deep reinforcement learning, DRL, network to track an object and navigate a robot in an environment, the system comprising: • a memory storage comprising instructions, and • the processor in communication with the memory storage, wherein the processor executes the instructions to perform the following method steps: ∘ receiving tracking sensor input representing the object and the environment at multiple times (step 1310); ∘ responsive to the tracking sensor input, calculating positions of the robot and the object at the multiple times (step 1320); ∘ computing (step 1330) a tracking quality reward r k at the multiple times according to the following equation: r k = Size observed Size object ⋅ Dist x t 0 − C tx L tx ⋅ Dist y t 0 − C ty L ty ∘ where Size (object) and Size (observed) are sizes, in terms of pixel numbers, of the object and the actually observed portion of the object in an input sensing frame I t, respectively, ∘ C tx and C ty are an ideal horizontal and an ideal vertical location in the input sensing frame I t, respectively, ∘ L tx is a normalization factor for the horizontal direction and L ty is a normalization factor for the vertical direction, ∘ x to is a horizontal location of the object in the input sensing frame I t and y to is a vertical location of the object in the input sensing frame I t, and ∘ Dist is a distance measurement to the object; ∘ computing, at the multiple times, a navigation path quality reward r p as a function of a shortest distance between the object and the robot (step 1340); and ∘ training the DRL network responsive to the tracking quality reward r k and the navigation path quality reward r p over multiple actions of moving the robot (step 1360). • a memory storage comprising instructions, and • the processor in communication with the memory storage, wherein the processor executes the instructions to perform the following method steps: ∘ receiving tracking sensor input representing the object and the environment at multiple times (step 1310); ∘ responsive to the tracking sensor input, calculating positions of the robot and the object at the multiple times (step 1320); ∘ computing (step 1330) a tracking quality reward r k at the multiple times according to the following equation: r k = Size observed Size object ⋅ Dist x t 0 − C tx L tx ⋅ Dist y t 0 − C ty L ty ∘ where Size (object) and Size (observed) are sizes, in terms of pixel numbers, of the object and the actually observed portion of the object in an input sensing frame I t, respectively, ∘ C tx and C ty are an ideal horizontal and an ideal vertical location in the input sensing frame I t, respectively, ∘ L tx is a normalization factor for the horizontal direction and L ty is a normalization factor for the vertical direction, ∘ x to is a horizontal location of the object in the input sensing frame I t and y to is a vertical location of the object in the input sensing frame I t, and ∘ Dist is a distance measurement to the object; ∘ computing, at the multiple times, a navigation path quality reward r p as a function of a shortest distance between the object and the robot (step 1340); and ∘ training the DRL network responsive to the tracking quality reward r k and the navigation path quality reward r p over multiple actions of moving the robot (step 1360). ∘ receiving tracking sensor input representing the object and the environment at multiple times (step 1310); ∘ responsive to the tracking sensor input, calculating positions of the robot and the object at the multiple times (step 1320); ∘ computing (step 1330) a tracking quality reward r k at the multiple times according to the following equation: r k = Size observed Size object ⋅ Dist x t 0 − C tx L tx ⋅ Dist y t 0 − C ty L ty ∘ where Size (object) and Size (observed) are sizes, in terms of pixel numbers, of the object and the actually observed portion of the object in an input sensing frame I t, respectively, ∘ C tx and C ty are an ideal horizontal and an ideal vertical location in the input sensing frame I t, respectively, ∘ L tx is a normalization factor for the horizontal direction and L ty is a normalization factor for the vertical direction, ∘ x to is a horizontal location of the object in the input sensing frame I t and y to is a vertical location of the object in the input sensing frame I t, and ∘ Dist is a distance measurement to the object; ∘ where Size (object) and Size (observed) are sizes, in terms of pixel numbers, of the object and the actually observed portion of the object in an input sensing frame I t, respectively, ∘ C tx and C ty are an ideal horizontal and an ideal vertical location in the input sensing frame I t, respectively, ∘ L tx is a normalization factor for the horizontal direction and L ty is a normalization factor for the vertical direction, ∘ x to is a horizontal location of the object in the input sensing frame I t and y to is a vertical location of the object in the input sensing frame I t, and ∘ Dist is a distance measurement to the object; ∘ computing, at the multiple times, a navigation path quality reward r p as a function of a shortest distance between the object and the robot (step 1340); and ∘ training the DRL network responsive to the tracking quality reward r k and the navigation path quality reward r p over multiple actions of moving the robot (step 1360).